@ai-on-browser/data-analysis-models

0.13.0

Returns accuracy.

accuracy(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: Accuracy

Returns precision with macro average.

precision(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: Precision

Returns recall with macro average.

recall(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: Recall

Returns F-score with macro average.

fScore(pred: Array<any>, t: Array<any>, beta: number): number
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
beta (number = 1) Positive real factor. Recall is considered beta times as important as precision.
Returns
number: F-score

Returns Cohen's kappa coefficient.

cohensKappa(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: Cohen's kappa coefficient

Returns Davies-Bouldin index.

davisBouldinIndex(data: Array<Array<number>>, pred: Array<any>, p: number, q: number): number
Parameters
data (Array<Array<number>>) Original data
pred (Array<any>) Predicted categories
p (number = 2) P
q (number = 1) Q
Returns
number: Davies-Bouldin index

silhouetteCoefficient

lib/evaluate/clustering.js

Returns Silhouette coefficient.

silhouetteCoefficient(data: Array<Array<number>>, pred: Array<any>): Array<number>
Parameters
data (Array<Array<number>>) Original data
pred (Array<any>) Predicted categories
Returns
Array<number>: Silhouette coefficient

Returns Dunn index.

dunnIndex(data: Array<Array<number>>, pred: Array<any>, intra_d: ("max" | "mean" | "centroid"), inter_d: "centroid"): number
Parameters
data (Array<Array<number>>) Original data
pred (Array<any>) Predicted categories
intra_d (("max" | "mean" | "centroid") = 'max') Intra-cluster distance type
inter_d ("centroid" = 'centroid') Inter-cluster distance type
Returns
number: Dunn index

Returns Purity.

purity(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: Purity

Returns Rand index.

randIndex(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: Rank index

Returns Dice index.

diceIndex(pred: Array<any>, t: Array<any>, beta: number): number
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
beta (number = 1) Positive real factor. Recall is considered beta times as important as precision.
Returns
number: Dice index

Returns Jaccard index.

jaccardIndex(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: Jaccard index

fowlkesMallowsIndex

lib/evaluate/clustering.js

Returns Fowlkes-Mallows index.

fowlkesMallowsIndex(pred: Array<any>, t: Array<any>): number
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: Fowlkes-Mallows index

Returns Co-Ranking Matrix.

coRankingMatrix(x: Array<Array<number>>, z: Array<Array<number>>, ks: number, kt: number): number
Parameters
x (Array<Array<number>>) Reduced values
z (Array<Array<number>>) Original values
ks (number) Rank significance
kt (number) Failure tolerance
Returns
number: Co-Ranking Matrix value

Returns MSE (Mean Squared Error).

Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Mean Squared Error

Returns RMSE (Root Mean Squared Error).

rmse(pred: (Array<number> | Array<Array<number>>), t: (Array<number> | Array<Array<number>>)): (number | Array<number>)
Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Root Mean Squared Error

Returns MAE (Mean Absolute Error).

Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Mean Absolute Error

Returns MAD (Median Absolute Deviation).

Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Median Absolute Deviation

Returns RMSPE (Root Mean Squared Percentage Error).

rmspe(pred: (Array<number> | Array<Array<number>>), t: (Array<number> | Array<Array<number>>)): (number | Array<number>)
Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Root Mean Squared Percentage Error

Returns MAPE (Mean Absolute Percentage Error).

mape(pred: (Array<number> | Array<Array<number>>), t: (Array<number> | Array<Array<number>>)): (number | Array<number>)
Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Mean Absolute Percentage Error

Returns MSLE (Mean Squared Logarithmic Error).

msle(pred: (Array<number> | Array<Array<number>>), t: (Array<number> | Array<Array<number>>)): (number | Array<number>)
Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Mean Squared Logarithmic Error

Returns RMSLE (Root Mean Squared Logarithmic Error).

rmsle(pred: (Array<number> | Array<Array<number>>), t: (Array<number> | Array<Array<number>>)): (number | Array<number>)
Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): RootMean Squared Logarithmic Error

Returns R2 (coefficient of determination).

Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Coefficient of determination

Returns correlation.

correlation(pred: (Array<number> | Array<Array<number>>), t: (Array<number> | Array<Array<number>>)): (number | Array<number>)
Parameters
pred ((Array<number> | Array<Array<number>>)) Predicted values
t ((Array<number> | Array<Array<number>>)) True values
Returns
(number | Array<number>): Correlation

Default export object.

default
Properties
Tensor (Tensor) : Tensor class
Matrix (Matrix) : Matrix class
Graph (Graph) : Graph class
Complex (Complex) : Complex number
Static Members
models
rl
evaluate

A2C agent

new A2CAgent(env: RLEnvironmentBase, resolution: number, procs: number, layers: Array<Object<string, any>>, optimizer: string)
Parameters
env (RLEnvironmentBase) Environment
resolution (number) Resolution of actions
procs (number) Number of processes
layers (Array<Object<string, any>>) Network layers
optimizer (string) Optimizer of the network
Instance Members
get_score()
get_action(state)
update(done, learning_rate, batch)

Adaptive Linear Neuron model

new ADALINE(rate: number)
Parameters
rate (number) Learning rate
Instance Members
init(train_x, train_y)
fit()
predict(data)

Adaptive Metric Nearest Neighbor

new ADAMENN(k0: number?, k1: number, k2: number?, l: number?, k: number, c: number)
Parameters
k0 (number? = null) The number of neighbors of the test point
k1 (number = 3) The number of neighbors in N1 for estimation
k2 (number? = null) The size of the neighborhood N2 for each of the k0 neighbors for estimation
l (number? = null) The number of points within the delta intervals
k (number = 3) The number of neighbors in the final nearest neighbor rule
c (number = 0.5) The positive factor for the exponential weighting scheme
Instance Members
fit(x, y)
predict(datas)

Adaptive thresholding

new AdaptiveThresholding(method: ("mean" | "gaussian" | "median" | "midgray"), k: number, c: number)
Parameters
method (("mean" | "gaussian" | "median" | "midgray") = 'mean') Method name
k (number = 3) Size of local range
c (number = 2) Value subtracted from threshold
Instance Members
predict(x)

Affinity propagation model

new AffinityPropagation()
Instance Members
categories
centroids
size
epoch
init(datas)
fit()
predict()

AgglomerativeClusterNode

lib/model/agglomerative.js
AgglomerativeClusterNode

Type: object

Properties
point (Array<number>?) : Data point of leaf node
index (number?) : Data index of leaf node
distance (number?) : Distance between children nodes
distances (Array<number>?) : Distances of leaf data and others
size (number) : Number of leaf nodes
children (Array<AgglomerativeClusterNode>?) : Children nodes
leafs (Array<AgglomerativeClusterNode>) : Leaf nodes

AgglomerativeClustering

lib/model/agglomerative.js

Agglomerative clustering

new AgglomerativeClustering(metric: ("euclid" | "manhattan" | "chebyshev"))
Parameters
metric (("euclid" | "manhattan" | "chebyshev") = 'euclid') Metric name
Instance Members
fit(points)
getClusters(number)
predict(k)
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

CompleteLinkageAgglomerativeClustering

lib/model/agglomerative.js

Complete linkage agglomerative clustering

new CompleteLinkageAgglomerativeClustering()

Extends AgglomerativeClustering

Instance Members
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

SingleLinkageAgglomerativeClustering

lib/model/agglomerative.js

Single linkage agglomerative clustering

new SingleLinkageAgglomerativeClustering()

Extends AgglomerativeClustering

Instance Members
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

GroupAverageAgglomerativeClustering

lib/model/agglomerative.js

Group average agglomerative clustering

new GroupAverageAgglomerativeClustering()

Extends AgglomerativeClustering

Instance Members
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

WardsAgglomerativeClustering

lib/model/agglomerative.js

Ward's agglomerative clustering

new WardsAgglomerativeClustering()

Extends AgglomerativeClustering

Instance Members
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

CentroidAgglomerativeClustering

lib/model/agglomerative.js

Centroid agglomerative clustering

new CentroidAgglomerativeClustering()

Extends AgglomerativeClustering

Instance Members
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

WeightedAverageAgglomerativeClustering

lib/model/agglomerative.js

Weighted average agglomerative clustering

new WeightedAverageAgglomerativeClustering()

Extends AgglomerativeClustering

Instance Members
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

MedianAgglomerativeClustering

lib/model/agglomerative.js

Median agglomerative clustering

new MedianAgglomerativeClustering()

Extends AgglomerativeClustering

Instance Members
distance(c1, c2)
update(ca, cb, ck, ka, kb, ab)

AkimaInterpolation

lib/model/akima.js

Akima interpolation

new AkimaInterpolation(modified: boolean)
Parameters
modified (boolean = false) Use modified method or not
Instance Members
fit(x, y)
predict(target)

Approximate Large Margin algorithm

new ALMA(p: number, alpha: number, b: number, c: number)
Parameters
p (number = 2) Power parameter for norm
alpha (number = 1) Degree of approximation to the optimal margin hyperplane
b (number = 1) Tuning parameter
c (number = 1) Tuning parameter
Related
A New Approximate Maximal Margin Classification Algorithm. (2001)
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Averaged One-Dependence Estimators

new AODE(discrete: number)
Parameters
discrete (number = 20) Discretized number
Instance Members
fit(datas, y)
predict(data)

Autoregressive model

new AR(p: number, method: ("lsm" | "yuleWalker" | "levinson" | "householder"))
Parameters
p (number) Order
method (("lsm" | "yuleWalker" | "levinson" | "householder") = lms) Method name
Instance Members
fit(data)
predict(data, k)

Autoregressive moving average model

new ARMA(p: number, q: number)
Parameters
p (number) Order of AR
q (number) Order of MA
Instance Members
fit(data)
predict(data, k)

Adaptive regularization of Weight Vectors

new AROW(r: number)
Parameters
r (number = 0.1) Learning rate
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Adaptive resonance theory

new ART(t: number, method: "l2")
Parameters
t (number = 1) Threshold
method ("l2" = 'l2') Method name
Instance Members
size
fit(datas)
predict(datas)

Apriori algorithm

new Apriori(minsup: number)
Parameters
minsup (number) Minimum support
Instance Members
predict(x)

Association analysis

new AssociationAnalysis(support: number)
Parameters
support (number) Minimum support
Instance Members
fit(x)
items(n)
support(a)
confidence(a, b)
lift(a, b)

Autoencoder

new Autoencoder(input_size: number, reduce_size: number, enc_layers: Array<Object<string, any>>, dec_layers: Array<Object<string, any>>, optimizer: string)
Parameters
input_size (number) Input size
reduce_size (number) Reduced dimension
enc_layers (Array<Object<string, any>>) Layers of encoder
dec_layers (Array<Object<string, any>>) Layers of decoder
optimizer (string) Optimizer of the network
Instance Members
epoch
fit(train_x, iteration, rate, batch, rho)
predict(x)
reduce(x)

Automatic thresholding

new AutomaticThresholding()
Instance Members
fit(x)
predict(x)

Average shifted histogram

new AverageShiftedHistogram(config: object, step: number)
Parameters
config (object) Config
step (number) Number of bins to average
Instance Members
fit(datas)
predict(datas)

BalancedHistogramThresholding

lib/model/balanced_histogram.js

Balanced histogram thresholding

new BalancedHistogramThresholding(minCount: number)
Parameters
minCount (number = 500) Minimum data count
Instance Members
predict(x)

Ballseptron

new Ballseptron(r: number)
Parameters
r (number) Radius
Instance Members
init(train_x, train_y)
fit()
predict(data)

Banditron

new Banditron(gamma: number)
Parameters
gamma (number = 0.5) Gamma
Instance Members
init(train_x, train_y)
fit()
predict(data)

BayesianLinearRegression

lib/model/bayesian_linear.js

Bayesian linear regression

new BayesianLinearRegression(lambda: number, sigma: number)
Parameters
lambda (number = 0.1) Tuning parameter
sigma (number = 0.2) Initial sigma of normal distribution
Instance Members
fit(x, y)
predict(x)

Bayesian Network

new BayesianNetwork(alpha: number)
Parameters
alpha (number) Equivalent sample size
Instance Members
fit(x)
probability(x)

BernsenThresholding

lib/model/bernsen.js

Bernsen thresholding

new BernsenThresholding(n: number, ct: number)
Parameters
n (number = 3) Size of local range
ct (number = 15) Minimum value of contrast
Instance Members
predict(x)

Bessel filter

new BesselFilter(n: number, c: number)

Extends LowpassFilter

Parameters
n (number = 2) Order
c (number = 0.5) Cutoff rate

Bilinear interpolation

new BilinearInterpolation()
Instance Members
fit(values, grids)
predict(x)

Balanced iterative reducing and clustering using hierarchies

new BIRCH(k: number, b: number, t: number, l: number)
Parameters
k (number)
b (number = 10) Maximum number of entries for each non-leaf nodes
t (number = 0.2) Threshold
l (number = Infinity) Maximum number of entries for each leaf nodes
Instance Members
fit(datas)
predict(datas)

Box-Cox transformation

new BoxCox(lambda: number?)
Parameters
lambda (number? = null) Lambda
Instance Members
fit(x)
predict(x)

Brahmagupta interpolation

new BrahmaguptaInterpolation()
Instance Members
fit(x, y)
predict(target)

Budgeted Stochastic Gradient Descent

new BSGD(b: number, eta: number, lambda: number, maintenance: ("removal" | "projection" | "merging"), kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))
Parameters
b (number = 10) Budget size
eta (number = 1) Learning rate
lambda (number = 1) Regularization parameter
maintenance (("removal" | "projection" | "merging") = removal) Maintenance type
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
init(train_x, train_y)
fit()
predict(data)

Budget Perceptron

new BudgetPerceptron(beta: number, n: number)
Parameters
beta (number) Tolerance
n (number = 0) Cachs size
Instance Members
init(train_x, train_y)
fit()
predict(data)

ButterworthFilter

lib/model/butterworth.js

Butterworth filter

new ButterworthFilter(n: number, c: number)

Extends LowpassFilter

Parameters
n (number = 2) Order
c (number = 0.5) Cutoff rate

Clustering based on Closest Pairs

new C2P(r: number, m: number)
Parameters
r (number) Number of representative points
m (number) Number of required sub-clusters
Instance Members
fit(data)
getClusters(number)
predict(k)

Canny edge detection

new Canny(th1: number, th2: number)
Parameters
th1 (number) Big threshold
th2 (number) Small threshold
Instance Members
predict(x)

Clustering Affinity Search Technique

new CAST(t: number)
Parameters
t (number) Affinity threshold
Instance Members
size
fit(datas)
predict()

Categorical naive bayes

new CategoricalNaiveBayes(alpha: number)
Parameters
alpha (number = 1.0) Smoothing parameter
Instance Members
fit(datas, labels)
probability(datas)
predict(datas)

CatmullRomSplines

lib/model/catmull_rom.js

Catmull-Rom splines interpolation

new CatmullRomSplines()
Instance Members
fit(x, y)
predict(target)

CentripetalCatmullRomSplines

lib/model/catmull_rom.js

Centripetal Catmull-Rom splines interpolation

new CentripetalCatmullRomSplines(alpha: number)
Parameters
alpha (number = 0.5) Number for knot parameterization
Instance Members
fit(x, y)
predict(target)

CHAMELEON

new CHAMELEON(k: number)
Parameters
k (number = 5) Number of neighborhoods
Instance Members
fit(datas)
getClusters(number)
predict(k)

Change finder

new ChangeFinder(p: number, r: number, smooth: number)
Deprecated: Does not work properly
Parameters
p (number = 1) Order
r (number = 0.5) Forgetting factor
smooth (number = 10) Smoothing window size
Instance Members
fit(datas)
predict()

ChebyshevFilter

lib/model/chebyshev.js

Chebyshev filter

new ChebyshevFilter(type: (1 | 2), ripple: number, n: number, c: number)

Extends LowpassFilter

Parameters
type ((1 | 2) = 1) Type number
ripple (number = 1) Ripple factor
n (number = 2) Order
c (number = 0.5) Cutoff rate

Clustering LARge Applications

new CLARA(k: number)
Parameters
k (number) Number of clusters
Instance Members
init(datas)
fit()
predict()

Clustering Large Applications based on RANdomized Search

new CLARANS(k: number)
Parameters
k (number) Number of clusters
Instance Members
init(datas)
fit(numlocal, maxneighbor)
predict()

CLUstEring based on local Shrinking

new CLUES(alpha: number)
Parameters
alpha (number = 0.05) Speed factor
Instance Members
size
fit(datas)
predict()

Co-training

new CoTraining(view1: object, view2: object)
Parameters
view1 (object) View
view2 (object) View
Instance Members
init(x, y)
fit()
predict()

Connectivity-based Outlier Factor

new COF(k: number)
Parameters
k (number) Number of neighborhoods
Instance Members
predict(datas)

Complement Naive Bayes

new ComplementNaiveBayes(distribution: "gaussian")
Parameters
distribution ("gaussian" = gaussian) Distribution name
Instance Members
fit(datas, labels)
predict(data)

Confidence weighted

new ConfidenceWeighted(eta: number)
Parameters
eta (number) Confidence value
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

SoftConfidenceWeighted

lib/model/confidence_weighted.js

Soft confidence weighted

new SoftConfidenceWeighted(eta: number, cost: number, v: (1 | 2))

Extends ConfidenceWeighted

Parameters
eta (number) Confidence value
cost (number) Tradeoff value between passiveness and aggressiveness
v ((1 | 2)) Version number

Cosine interpolation

new CosineInterpolation()
Instance Members
fit(x, y)
predict(target)

Conditional random fields

new CRF()
Instance Members
fit(x, y)
probability(x, y)
predict(x)

CubicConvolutionInterpolation

lib/model/cubic_convolution.js

Cubic-convolution interpolation

new CubicConvolutionInterpolation(a: number)
Parameters
a (number) Tuning parameter
Instance Members
fit(values)
predict(index)

Cubic Hermite spline

new CubicHermiteSpline(t: number, b: number)
Parameters
t (number) Tension factor
b (number) Bias factor
Instance Members
fit(x, y)
predict(target)

Cubic interpolation

new CubicInterpolation()
Instance Members
fit(x, y)
predict(target)

Cumulative moving average

new CumulativeMovingAverage()
Instance Members
predict(data)

Cumulative sum change point detection

new CumSum()
Instance Members
init(datas)
fit()
predict()
CURENode

Type: object

Properties
point (Array<number>?) : Data point of leaf node
index (number?) : Data index of leaf node
distance (number?) : Distance between children nodes
size (number) : Number of leaf nodes
children (Array<CURENode>?) : Children nodes
leafs (Array<CURENode>) : Leaf nodes

Clustering Using REpresentatives

new CURE(c: number)
Parameters
c (number) Number of representative points
Instance Members
fit(data)
getClusters(number)
predict(k)

DiscriminantAdaptiveNearestNeighbor

lib/model/dann.js

Discriminant adaptive nearest neighbor

new DiscriminantAdaptiveNearestNeighbor(k: number)
Parameters
k (number = null) Number of neighborhoods
Instance Members
fit(x, y)
predict(data, iteration)

Density-based spatial clustering of applications with noise

new DBSCAN(eps: number, minPts: number, metric: ("euclid" | "manhattan" | "chebyshev"))
Parameters
eps (number = 0.5) Radius to determine neighborhood
minPts (number = 5) Minimum size of cluster
metric (("euclid" | "manhattan" | "chebyshev") = euclid) Metric name
Instance Members
predict(datas)

Decision tree

new DecisionTree()
Instance Members
depth
init(datas, targets)
fit()
importance()
predict_value(data)

DecisionTreeClassifier

lib/model/decision_tree.js

Decision tree classifier

new DecisionTreeClassifier(method: ("ID3" | "CART"))

Extends DecisionTree

Parameters
method (("ID3" | "CART")) Method name
Instance Members
predict_prob(data)
predict(data)

DecisionTreeRegression

lib/model/decision_tree.js

Decision tree regression

new DecisionTreeRegression()

Extends DecisionTree

Instance Members
predict(data)

Delaunay interpolation

new DelaunayInterpolation()
Instance Members
fit(x, y)
predict(x)

DemingRegression

lib/model/deming.js

Deming regression

new DemingRegression(d: number)
Parameters
d (number) Ratio of variances
Instance Members
fit(x, y)
predict(x)

DENsity CLUstering

new DENCLUE(h: number, version: (1 | 2), kernel: ("gaussian" | function (Array<number>): number))
Parameters
h (number) Smoothing parameter for the kernel
version ((1 | 2) = 1) Version number
kernel (("gaussian" | function (Array<number>): number) = gaussian) Kernel name
Instance Members
size
init(datas)
fit()
predict()

DIvisive ANAlysis Clustering

new DIANA()
Instance Members
size
init(datas)
fit()
predict()

Diffusion map

new DiffusionMap(t: number, kernel: ("gaussian" | function (Array<number>, Array<number>): number))
Parameters
t (number) Power parameter
kernel (("gaussian" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
predict(x, rd)

Deep Q-Network agent

new DQNAgent(env: RLEnvironmentBase, resolution: number, layers: Array<Object<string, any>>, optimizer: string)
Parameters
env (RLEnvironmentBase) Environment
resolution (number) Resolution of actions
layers (Array<Object<string, any>>) Network layers
optimizer (string) Optimizer of the network
Instance Members
method
get_score()
get_action(state, greedy_rate)
update(action, state, next_state, reward, done, learning_rate, batch)

Dynamic programming agent

new DPAgent(env: RLEnvironmentBase, resolution: number)
Parameters
env (RLEnvironmentBase) Environment
resolution (number = 20) Resolution
Instance Members
get_score()
get_action(state)
update(method)

Elastic net

new ElasticNet(lambda: number, alpha: number, method: ("ISTA" | "CD"))
Parameters
lambda (number = 0.1) Regularization strength
alpha (number = 0.5) Mixing parameter
method (("ISTA" | "CD") = CD) Method name
Instance Members
fit(x, y)
predict(x)
importance()

Elliptic filter

new EllipticFilter(ripple: number, n: number, xi: number, c: number)

Extends LowpassFilter

Parameters
ripple (number = 1) Ripple factor
n (number = 2) Order
xi (number = 1) Selectivity factor
c (number = 0.5) Cutoff rate

Extended Nearest Neighbor

new ENN(version: (0 | 1 | 2), k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))
Parameters
version ((0 | 1 | 2) = 1) Version
k (number = 5) Number of neighborhoods
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
fit(datas, targets)
predict(datas)
BinaryModel

Type: object

Properties
init (function (Array<Array<number>>, Array<any>): void) : Initialize model
fit (function (...any): void) : Fit model
predict (function (Array<Array<number>>): Array<number>) : Returns predicted values

EnsembleBinaryModel

lib/model/ensemble_binary.js

Ensemble binary models

new EnsembleBinaryModel(model: any, type: ("oneone" | "onerest"), classes: Array<any>?)
Parameters
model (any)
type (("oneone" | "onerest")) Type name
classes (Array<any>?) Initial class labels
Instance Members
init(train_x, train_y)
fit(x, y, args)
predict(data)

ExponentialMovingAverage

lib/model/exponential_average.js

Exponential moving average

new ExponentialMovingAverage()
Instance Members
predict(data, k)

Modified moving average

new ModifiedMovingAverage()
Instance Members
predict(data, k)

Bsae class for Extremely Randomized Trees

new ExtraTrees(tree_num: number, sampling_rate: number)
Parameters
tree_num (number) Number of trees
sampling_rate (number = 1.0) Sampling rate
Instance Members
depth
init(datas, targets)
fit()

ExtraTreesClassifier

lib/model/extra_trees.js

Extra trees classifier

new ExtraTreesClassifier(tree_num: number, sampling_rate: number)

Extends ExtraTrees

Parameters
tree_num (number) Number of trees
sampling_rate (number = 1.0) Sampling rate
Instance Members
predict(datas)

ExtraTreesRegressor

lib/model/extra_trees.js

Extra trees regressor

new ExtraTreesRegressor(tree_num: number, sampling_rate: number)

Extends ExtraTrees

Parameters
tree_num (number) Number of trees
sampling_rate (number = 1.0) Sampling rate
Instance Members
predict(datas)

FastMap

new FastMap()
Instance Members
predict(x, rd)

Forgetron

new Forgetron(b: number, kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))
Parameters
b (number) Budget parameter
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
init(train_x, train_y)
fit()
predict(data)

Fuzzy c-means

new FuzzyCMeans(m: number)
Parameters
m (number = 2) Fuzziness factor
Instance Members
init(datas)
add()
fit()
predict()

Fuzzy k-nearest neighbor

new FuzzyKNN(k: number, m: number)
Parameters
k (number = 5) Number of neighborhoods
m (number = 2) Factor of weight for distance
Instance Members
categories
add(point, category?)
fit(datas, targets)
predict(datas)

Generative adversarial networks

new GAN(noise_dim: number, g_hidden: Array<Object<string, any>>, d_hidden: Array<Object<string, any>>, g_opt: string, d_opt: string, class_size: (number | null), type: ("" | "conditional"))
Parameters
noise_dim (number) Number of noise dimension
g_hidden (Array<Object<string, any>>) Layers of generator
d_hidden (Array<Object<string, any>>) Layers of discriminator
g_opt (string) Optimizer of the generator network
d_opt (string) Optimizer of the discriminator network
class_size ((number | null)) Class size for conditional type
type (("" | "conditional")) Type name
Instance Members
epoch
fit(x, y, step, gen_rate, dis_rate, batch)
prob(x, y)
generate(n, y)

Gasser–Müller kernel estimator

new GasserMuller(h: number)
Parameters
h (number) Smoothing parameter for the kernel
Instance Members
fit(x, y)
predict(x)

Gaussian process

new GaussianProcess(kernel: "gaussian", beta: number)
Parameters
kernel ("gaussian" = gaussian) Kernel name
beta (number = 1) Precision parameter
Instance Members
init(x, y)
fit(learning_rate)
predict(x)

Gradient boosting decision tree

new GBDT(maxdepth: number, srate: number, lr: number)
Parameters
maxdepth (number = 1) Maximum depth of tree
srate (number = 1.0) Sampling rate
lr (number = 0) Learning rate
Instance Members
size
init(x, y)
fit()
predict(x)

GBDTClassifier

lib/model/gbdt.js

Gradient boosting decision tree classifier

new GBDTClassifier(maxdepth: number, srate: number, lr: number)

Extends GBDT

Parameters
maxdepth (number = 1) Maximum depth of tree
srate (number = 1.0) Sampling rate
lr (number = 0) Learning rate
Instance Members
init(x, y)
predict(x)

Generalized extreme studentized deviate

new GeneralizedESD(alpha: number, r: number)
Parameters
alpha (number) Significance level
r (number) Max number of outliers
Instance Members
predict(data)
GeneticModel

Type: object

Properties
run (function (...any): void) : Run model
mutation (function (): GeneticModel) : Returns mutated model
mix (function (GeneticModel): GeneticModel) : Returns mixed model
score (function (): number) : Returns a number how good the model is

Genetic algorithm

new GeneticAlgorithm(size: number, model: any)
Parameters
size (number) Number of models per generation
model (any)
Instance Members
bestModel
run(args)
next(mutation_rate)

GeneticAlgorithmGeneration

lib/model/genetic_algorithm.js

Genetic algorithm generation

new GeneticAlgorithmGeneration(env: RLEnvironmentBase, size: number, resolution: number)
Parameters
env (RLEnvironmentBase) Environment
size (number = 100) Number of models per generation
resolution (number = 20) Resolution
Instance Members
reset()
get_score()
top_agent()
run()
next(mutation_rate)

Genetic k-means model

new GeneticKMeans(k: number, size: number)
Parameters
k (number) Number of clusters
size (number) Number of models per generation
Instance Members
centroids
bestModel
init(datas)
predict(datas)
fit()

G-means

new GMeans()
Instance Members
centroids
size
clear()
fit(datas, iterations)
predict(datas)

Gaussian mixture model

new GMM()
Instance Members
add()
clear()
probability(data)
predict(data)
fit(datas)

SemiSupervisedGMM

lib/model/gmm.js

Semi-Supervised gaussian mixture model

new SemiSupervisedGMM()

Extends GMM

Instance Members
categories
init(datas, labels)
fit(datas, y)
predict(data)

Gaussian mixture regression

new GMR()

Extends GMM

Instance Members
add()
clear()
fit(x, y)
probability(x, y)
predict(x)

Gaussian Process Latent Variable Model

new GPLVM(rd: number, alpha: number, ez: number, ea: number, ep: number, kernel: "gaussian", kernelArgs: Array<any>?)
Parameters
rd (number) Reduced dimension
alpha (number) Precision parameter
ez (number = 1.0) Learning rate for z
ea (number = 0.005) Learning rate for alpha
ep (number = 0.2) Learning rate for kernel
kernel ("gaussian" = gaussian) Kernel name
kernelArgs (Array<any>? = []) Arguments for kernel
Instance Members
init(x)
fit()
llh()
predict()
reconstruct(z)

Growing cell structures

new GrowingCellStructures()
Instance Members
size
update(x)
fit(x)
predict(datas)

Growing neural gas

new GrowingNeuralGas(l: number, m: number)
Parameters
l (number) Neughborhood range
m (number) Decreasing factor of l
Instance Members
size
update(x)
fit(x)
predict(datas)

Generative topographic mapping

new GTM(input_size: number, output_size: number, k: number, q: number)
Parameters
input_size (number) Input size
output_size (number) Output size
k (number = 20) Grid size
q (number = 10) Grid size for basis function
Instance Members
probability(x)
responsibility(x)
fit(data)
predictIndex(x)
predict(x)

Hampel filter

new HampelFilter(k: number, th: number)
Parameters
k (number = 3) Half window size
th (number = 3) Threshold
Instance Members
predict(x)

Hierarchical Density-based spatial clustering of applications with noise

new HDBSCAN(minClusterSize: number, minPts: number, metric: ("euclid" | "manhattan" | "chebyshev"))
Parameters
minClusterSize (number = 5) Minimum number of clusters to be recognized as a cluster
minPts (number = 5) Number of neighborhood with core distance
metric (("euclid" | "manhattan" | "chebyshev") = euclid) Metric name
Instance Members
size
predict(datas)

Histogram

new Histogram(config: object?)
Parameters
config (object? = {}) Config
Instance Members
fit(datas)
predict(datas)

Hessian Locally Linear Embedding

new HLLE(k: number)
Parameters
k (number = 1) Number of neighborhoods
Instance Members
predict(x, rd)

Hidden Markov model

new HMMBase(n: number)
Parameters
n (number) Number of states
Instance Members
probability(x)
bestPath(x)

Hidden Markov model

new HMM(n: number)

Extends HMMBase

Parameters
n (number) Number of states
Instance Members
fit(datas, scaled)
probability(datas)
bestPath(data)

ContinuousHMM

lib/model/hmm.js

Continuous hidden Markov model

new ContinuousHMM(n: number, d: number)

Extends HMMBase

Parameters
n (number) Number of states
d (number) Number of data dimensions
Instance Members
fit(x, scaled)
probability(datas)
bestPath(data)
generate(n, length)

HMMClassifier

lib/model/hmm.js

Hidden Markov model classifier

new HMMClassifier(classes: Array<number>, states: number, cls: any)
Parameters
classes (Array<number>) Initial class labels
states (number) Number of states
cls (any = ContinuousHMM) HMM class
Instance Members
fit(x, y, scaled)
predict(x)

Holt-Winters method

new HoltWinters(a: number, b: number, g: number, s: number)
Parameters
a (number) Weight for last value
b (number = 0) Weight for trend value
g (number = 0) Weight for seasonal data
s (number = 0) Length of season
Instance Members
fit(x)
predict(k)

HopfieldNetwork

lib/model/hopfield.js

Hopfield network

new HopfieldNetwork()
Instance Members
fit(x)
energy(x)
predict(x)

Hotelling T-square Method

new Hotelling()
Instance Members
fit(data)
predict(data)

Huber regression

new HuberRegression(e: number, method: ("rls" | "gd"), lr: number)
Parameters
e (number = 1.35) Threshold of outliers
method (("rls" | "gd") = rls) Method name
lr (number = 1) Learning rate
Instance Members
fit(x, y)
predict(x)

Independent component analysis

new ICA()
Instance Members
fit(x)
predict(x, rd)

Classical ellipsoid method

new CELLIP(gamma: number, a: number)
Parameters
gamma (number = 0.1) Desired classification margin
a (number = 0.5) Tradeoff parameter
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Improved ellipsoid method

new IELLIP(b: number, c: number)
Parameters
b (number = 0.9) Parameter controlling the memory of online learning
c (number = 0.5) Parameter controlling the memory of online learning
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Locally Informative K-Nearest Neighbor

new IKNN(k: number, i: number)
Parameters
k (number) Number of neighbors
i (number) Number of informative points
Instance Members
fit(x, y)
predict(data)

Incremental principal component analysis

new IncrementalPCA(f: number)
Parameters
f (number = 0.95) Forgetting factor
Instance Members
update(x)
fit(x)
predict(x, rd)

Influenced Outlierness

new INFLO(k: number)
Parameters
k (number) Number of neighborhoods
Instance Members
predict(datas)

Inverse distance weighting

new InverseDistanceWeighting(k: number, p: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))
Parameters
k (number = 5) Number of neighborhoods
p (number = 2) Power parameter
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
fit(x, y)
predict(data)

InverseSmoothstepInterpolation

lib/model/inverse_smoothstep.js

Inverse smoothstep interpolation

new InverseSmoothstepInterpolation()
Instance Members
fit(x, y)
predict(target)

Iterative Self-Organizing Data Analysis Technique

new ISODATA(init_k: number, min_k: number, max_k: number, min_n: number, split_std: number, merge_dist: number)
Parameters
init_k (number) Initial cluster count
min_k (number) Minimum cluster count
max_k (number) Maximum cluster count
min_n (number) Minimum cluster size
split_std (number) Standard deviation as splid threshold
merge_dist (number) Merge distance
Instance Members
centroids
size
init(data)
fit(data)
predict(datas)

Isolation forest

new IsolationForest(tree_num: number, sampling_rate: number)
Parameters
tree_num (number = 100) Number of trees
sampling_rate (number = 0.8) Sampling rate
Instance Members
fit(datas)
predict(datas)

Isomap

new Isomap(neighbors: number)
Parameters
neighbors (number = 0) Number of neighborhoods
Instance Members
predict(x, rd)

IsotonicRegression

lib/model/isotonic.js

Isotonic regression

new IsotonicRegression()
Instance Members
fit(x, y)
predict(x)

Kalman filter

new KalmanFilter()
Instance Members
fit(z)
predict(k)

Kernel Density Estimation Outlier Score

new KDEOS(kmin: number, kmax: number, kernel: ("gaussian" | "epanechnikov" | function (number, number, number): number))
Parameters
kmin (number) Minimum number of neighborhoods
kmax (number) Maximum number of neighborhoods
kernel (("gaussian" | "epanechnikov" | function (number, number, number): number) = gaussian) Kernel name
Instance Members
predict(datas)

Kernel density estimator

new KernelDensityEstimator(kernel: ("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "biweight" | "triweight" | function (number): number))
Parameters
kernel (("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "biweight" | "triweight" | function (number): number) = gaussian) Kernel name
Instance Members
fit(x, h)
probability(x)
predict(x)

Kernel k-means

new KernelKMeans(k: number)
Parameters
k (number = 3) Number of clusters
Instance Members
init(datas)
predict()
fit()

Kernelized Primal Estimated sub-GrAdientSOlver for SVM

new KernelizedPegasos(rate: number, kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))
Parameters
rate (number) Learning rate
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
init(train_x, train_y)
fit()
predict(data)

Kernelized perceptron

new KernelizedPerceptron(rate: number, kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))
Parameters
rate (number = 1) Learning rate
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
init(train_x, train_y)
predict(data)

Kullback-Leibler importance estimation procedure

new KLIEP(sigma: Array<number>, fold: number, kernelNum: number)
Parameters
sigma (Array<number>) Sigmas of normal distribution
fold (number) Number of folds
kernelNum (number) Number of kernels
Instance Members
fit(x1, x2)
predict(x)

Bsae class for k-means like model

new KMeansBase()
Instance Members
centroids
size
add(datas)
clear()
predict(datas)
fit(datas)

k-means model

new KMeans()

Extends KMeansBase

Instance Members
_add(centroids, datas)
_move(centroids, datas)

k-means++ model

new KMeanspp()

Extends KMeans

Instance Members
_add(centroids, datas)

k-medoids model

new KMedoids()

Extends KMeans

Instance Members
_move(centroids, datas)

k-medians model

new KMedians()

Extends KMeans

Instance Members
_move(centroids, datas)

SemiSupervisedKMeansModel

lib/model/kmeans.js

semi-supervised k-means model

new SemiSupervisedKMeansModel()

Extends KMeansBase

Instance Members
categories
init(datas, labels)
fit(datas, labels)
predict(datas)

k-modes model

new KModes()
Instance Members
size
add(datas)
clear()
predict(datas)
fit(datas)

Bsae class for k-nearest neighbor models

new KNNBase(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))
Parameters
k (number = 5) Number of neighborhoods
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
_add(point, category?)

k-nearest neighbor

new KNN(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends KNNBase

Parameters
k (number = 5) Number of neighborhoods
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point, category)
fit(datas, targets)
predict(datas)

k-nearest neighbor regression

new KNNRegression(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends KNNBase

Parameters
k (number = 5) Number of neighborhoods
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point, category)
fit(datas, targets)
predict(datas)

k-nearest neighbor anomaly detection

new KNNAnomaly(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends KNNBase

Parameters
k (number = 5) Number of neighborhoods
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point)
fit(datas)
predict(datas)

KNNDensityEstimation

lib/model/knearestneighbor.js

k-nearest neighbor density estimation

new KNNDensityEstimation(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends KNNBase

Parameters
k (number = 5) Number of neighborhoods
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point)
fit(datas)
predict(datas)

Semi-supervised k-nearest neighbor

new SemiSupervisedKNN(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends KNNBase

Parameters
k (number = 5) Number of neighborhoods
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point, category)
fit(datas, targets)
predict()

k-prototypes model

new KPrototypes(gamma: number, categoryPositions: Array<boolean>)
Parameters
gamma (number) Weight for categorical data
categoryPositions (Array<boolean>) Category column position
Instance Members
size
add(datas)
clear()
predict(datas)
fit(datas)

k-SVD

new KSVD(x: Array<Array<number>>, m: number, k: number?)
Parameters
x (Array<Array<number>>) Training data
m (number) Reduced dimension
k (number? = m) Sparsity parameter
Instance Members
fit()
predict()

KolmogorovZurbenkoFilter

lib/model/kz.js

Kolmogorov–Zurbenko filter

new KolmogorovZurbenkoFilter(m: number, k: number)
Parameters
m (number) Window size
k (number) Iteration count of a moving average
Instance Members
predict(x)

Label propagation

new LabelPropagation(method: ("rbf" | "knn"), sigma: number, k: number)
Parameters
method (("rbf" | "knn") = rbf) Method name
sigma (number = 0.1) Sigma of normal distribution
k (number = Infinity) Number of neighborhoods
Instance Members
init(x, y)
fit()
predict()

Label spreading

new LabelSpreading(alpha: number, method: ("rbf" | "knn"), sigma: number, k: number)
Parameters
alpha (number = 0.2) Clamping factor
method (("rbf" | "knn") = rbf) Method name
sigma (number = 0.1) Sigma of normal distribution
k (number = Infinity) Number of neighborhoods
Instance Members
init(x, y)
fit()
predict()

Ladder network

new LadderNetwork(hidden_sizes: Array<number>, lambdas: Array<number>, activation: string, optimizer: string)
Parameters
hidden_sizes (Array<number>) Sizes of hidden layers
lambdas (Array<number>) Regularization parameters
activation (string) Activation name
optimizer (string) Optimizer of the network
Instance Members
epoch
fit(train_x, train_y, iteration, rate, batch)
predict(x)

LagrangeInterpolation

lib/model/lagrange.js

Lagrange interpolation

new LagrangeInterpolation(method: ("weighted" | "newton" | ""))
Parameters
method (("weighted" | "newton" | "") = weighted) Method name
Instance Members
fit(x, y)
predict(target)

Lanczos interpolation

new LanczosInterpolation(n: number)
Parameters
n (number) Order
Instance Members
fit(values)
predict(index)

Laplacian eigenmaps

new LaplacianEigenmaps(affinity: ("rbf" | "knn"), k: number, sigma: number, laplacian: ("unnormalized" | "normalized"))
Parameters
affinity (("rbf" | "knn") = rbf) Affinity type name
k (number = 10) Number of neighborhoods
sigma (number = 1) Sigma of normal distribution
laplacian (("unnormalized" | "normalized") = unnormalized) Normalized laplacian matrix or not
Instance Members
predict(x, rd)

Laplacian edge detection

new Laplacian(th: number, n: (4 | 8))
Parameters
th (number) Threshold
n ((4 | 8) = 4) Number of neighborhoods
Instance Members
predict(x)

Least absolute shrinkage and selection operator

new Lasso(lambda: number, method: ("CD" | "ISTA" | "LARS"))
Parameters
lambda (number = 1.0) Regularization strength
method (("CD" | "ISTA" | "LARS") = CD) Method name
Instance Members
fit(x, y)
predict(x)
importance()

Latent dirichlet allocation

new LatentDirichletAllocation(t: number)
Parameters
t (number = 2) Topic count
Instance Members
init(x)
predict()

Linde-Buzo-Gray algorithm

new LBG()
Instance Members
centroids
size
clear()
fit(datas)
predict(datas)

LinearDiscriminant

lib/model/lda.js

Linear discriminant analysis

new LinearDiscriminant()
Instance Members
init(train_x, train_y)
fit()
predict(data)

FishersLinearDiscriminant

lib/model/lda.js

Fishers linear discriminant analysis

new FishersLinearDiscriminant()
Instance Members
init(train_x, train_y)
fit()
predict(data)

MulticlassLinearDiscriminant

lib/model/lda.js

Multiclass linear discriminant analysis

new MulticlassLinearDiscriminant()
Instance Members
fit(x, y)
predict(data)

LinearDiscriminantAnalysis

lib/model/lda.js

Linear discriminant analysis

new LinearDiscriminantAnalysis()
Instance Members
predict(x, t, rd)

Local Density Factor

new LDF(k: number)
Parameters
k (number) Number of neighborhoods
Instance Members
predict(datas)

Local Distance-based Outlier Factor

new LDOF(k: number)
Parameters
k (number) Number of neighborhoods
Instance Members
predict(datas)

Least absolute deviations

new LeastAbsolute()
Instance Members
fit(x, y)
predict(x)

Least squares

new LeastSquares()
Instance Members
fit(x, y)
predict(x)

LinearInterpolation

lib/model/lerp.js

Linear interpolation

new LinearInterpolation()
Instance Members
fit(x, y)
predict(target)

Locally Linear Embedding

new LLE(k: number)
Parameters
k (number = 1) Number of neighborhoods
Instance Members
predict(x, rd)

LeastMedianSquaresRegression

lib/model/lmeds.js

Least median squares regression

new LeastMedianSquaresRegression(k: number)
Parameters
k (number = 5) Sampling count
Instance Members
fit(x, y)
predict(x)

Large Margin Nearest Neighbor

new LMNN(gamma: number, lambda: number)
Parameters
gamma (number) Tuning parameter
lambda (number) Tuning parameter
Instance Members
init(x, y)
fit()
predict(x)

Local Correlation Integral

new LOCI(alpha: number)
Parameters
alpha (number = 0.5) Alpha
Instance Members
predict(datas)

Local Outlier Factor

new LOF(k: number)
Parameters
k (number) Number of neighborhoods
Instance Members
predict(datas)

Laplacian of gaussian filter

new LoG(th: number)
Parameters
th (number) Threshold
Instance Members
predict(x)

Logarithmic interpolation

new LogarithmicInterpolation()
Instance Members
fit(x, y)
predict(target)

LogisticRegression

lib/model/logistic.js

Logistic regression

new LogisticRegression()
Instance Members
fit(x, y, iteration, rate, l1, l2)
predict(points)

MultinomialLogisticRegression

lib/model/logistic.js

Multinomial logistic regression

new MultinomialLogisticRegression(classes: Array<number>?)
Parameters
classes (Array<number>?) Initial class labels
Instance Members
fit(train_x, train_y, iteration, rate, l1, l2)
predict(points)

Local Outlier Probability

new LoOP(k: number)
Parameters
k (number) Number of neighborhoods
Instance Members
predict(datas)

Locally weighted scatter plot smooth

new LOWESS()
Instance Members
fit(x, y)
predict(x)

Lowpass filter

new LowpassFilter(c: number)
Parameters
c (number = 0.5) Cutoff rate
Instance Members
predict(x)

LpNormLinearRegression

lib/model/lpnorm_linear.js

Lp norm linear regression

new LpNormLinearRegression(p: number)
Parameters
p (number = 2) Power parameter for norm
Instance Members
fit(x, y)
predict(x)

Latent Semantic Analysis

new LSA()
Instance Members
predict(x, rd)

Least-squares density difference

new LSDD(sigma: Array<number>, lambda: Array<number>)
Parameters
sigma (Array<number>) Sigmas of normal distribution
lambda (Array<number>) Regularization parameters
Instance Members
fit(x1, x2)
predict(x)

LSDD for change point detection

new LSDDCPD(w: number, take: number?, lag: number?)
Parameters
w (number) Window size
take (number?) Take number
lag (number?) Lag
Instance Members
predict(datas)

least-squares importance fitting

new LSIF(sigma: Array<number>, lambda: Array<number>, fold: number, kernelNum: number)
Parameters
sigma (Array<number>) Sigmas of normal distribution
lambda (Array<number>) Regularization parameters
fold (number) Number of folds
kernelNum (number) Number of kernels
Instance Members
fit(x1, x2)
predict(x)

LeastTrimmedSquaresRegression

lib/model/lts.js

Least trimmed squares

new LeastTrimmedSquaresRegression(h: number)
Parameters
h (number = 0.9) Sampling rate
Instance Members
fit(x, y)
predict(x)

Learning Vector Quantization clustering

new LVQCluster(k: number)
Parameters
k (number) Number of clusters
Instance Members
fit(x, lr)
predict(datas)

LVQClassifier

lib/model/lvq.js

Learning Vector Quantization classifier

new LVQClassifier(type: (1 | 2 | 3))
Parameters
type ((1 | 2 | 3)) Type number
Instance Members
fit(x, y, lr)
predict(datas)

Median Absolute Deviation

new MAD()
Instance Members
fit(data)
predict(data)

Margin Perceptron

new MarginPerceptron(rate: number)
Parameters
rate (number) Learning rate
Instance Members
init(train_x, train_y)
fit()
predict(data)

Markov switching

new MarkovSwitching(regime: number)
Parameters
regime (number) Number of regime
Instance Members
fit(datas, eps, trial)
probability(datas)
predict(datas)

Max absolute scaler

new MaxAbsScaler()
Instance Members
fit(x)
predict(x)

MaximumLikelihoodEstimator

lib/model/maximum_likelihood.js

Maximum likelihood estimator

new MaximumLikelihoodEstimator(distribution: "normal")
Parameters
distribution ("normal" = normal) Distribution name
Instance Members
fit(x)
probability(x)
predict(x)

Minimum Covariance Determinant

new MCD(datas: Array<Array<number>>, sampling_rate: number)
Parameters
datas (Array<Array<number>>) Training data
sampling_rate (number) Sampling rate
Instance Members
fit()
predict(data)

MixtureDiscriminant

lib/model/mda.js

Mixture discriminant analysis

new MixtureDiscriminant(r: number)
Parameters
r (number) Number of components
Instance Members
init(x, y)
fit()
predict(data)

Multi-dimensional Scaling

new MDS()
Instance Members
predict(x, rd, dmat)

Mean shift

new MeanShift(h: number)
Parameters
h (number) Smoothing parameter for the kernel
Instance Members
categories
init(data)
predict(threshold)
fit()

MetropolisHastings

lib/model/mh.js

Metropolis-Hastings algorithm

new MetropolisHastings(targetFunc: function (Array<number>): number, d: number, q: "gaussian")
Parameters
targetFunc (function (Array<number>): number) Target distribution
d (number) Output size
q ("gaussian" = gaussian) Proposal density name
Instance Members
sample(n, t)

MinmaxNormalization

lib/model/minmax.js

Min-max normalization

new MinmaxNormalization(min: number, max: number)
Parameters
min (number = 0) Minimum value
max (number = 1) Maximum value
Instance Members
fit(x)
predict(x)

Margin Infused Relaxed Algorithm

new MIRA()
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Modified Locally Linear Embedding

new MLLE(k: number)
Parameters
k (number = 1) Number of neighborhoods
Instance Members
predict(data, rd)

MLPClassifier

lib/model/mlp.js

Multi layer perceptron classifier

new MLPClassifier(hidden_sizes: Array<number>, activation: string, optimizer: string)
Parameters
hidden_sizes (Array<number>) Sizes of hidden layers
activation (string) Activation name
optimizer (string) Optimizer of the network
Instance Members
epoch
fit(train_x, train_y, iteration, rate, batch)
predict(x)

MLPRegressor

lib/model/mlp.js

Multi layer perceptron regressor

new MLPRegressor(hidden_sizes: Array<number>, activation: string, optimizer: string)
Parameters
hidden_sizes (Array<number>) Sizes of hidden layers
activation (string) Activation name
optimizer (string) Optimizer of the network
Instance Members
epoch
fit(train_x, train_y, iteration, rate, batch)
predict(x)

Method of Optimal Direction

new MOD(x: Array<Array<number>>, m: number, k: number?)
Parameters
x (Array<Array<number>>) Training data
m (number) Reduced dimension
k (number? = m) Sparsity parameter
Instance Members
fit()
predict()

MONothetic Analysis Clustering

new MONA()
Instance Members
size
init(datas)
fit()
predict()

MonotheticClustering

lib/model/monothetic.js

Monothetic Clustering

new MonotheticClustering()
Instance Members
size
init(datas)
fit()
predict()

Monte Carlo agent

new MCAgent(env: RLEnvironmentBase, resolution: number)
Parameters
env (RLEnvironmentBase) Environment
resolution (number = 20) Resolution
Instance Members
reset()
get_score()
get_action(state, greedy_rate)
update(action, state, reward, done)

Mountain method

new Mountain(r: number, alpha: number, beta: number)
Parameters
r (number) Resolution of grid
alpha (number) Tuning parameter
beta (number) Tuning parameter
Instance Members
init(datas)
fit()
predict(data)

SimpleMovingAverage

lib/model/moving_average.js

Simple moving average

new SimpleMovingAverage()
Instance Members
predict(data, n)

LinearWeightedMovingAverage

lib/model/moving_average.js

Linear weighted moving average

new LinearWeightedMovingAverage()
Instance Members
predict(data, n)

TriangularMovingAverage

lib/model/moving_average.js

Triangular moving average

new TriangularMovingAverage()
Instance Members
predict(data, k)

Moving median

new MovingMedian()
Instance Members
predict(data, n)

Mahalanobis Taguchi method

new MT()
Instance Members
fit(data)
predict(data)

MutualInformationFeatureSelection

lib/model/mutual_information.js

Mutual information feature selector

new MutualInformationFeatureSelection()
Instance Members
fit(x, y)
predict(x, k)

Mutual k-nearest-neighbor model

new MutualKNN(k: number)
Parameters
k (number = 5) Number of neighborhoods
Instance Members
size
fit(datas)
predict()

n-cubic interpolation

new NCubicInterpolation()
Instance Members
fit(values, grids)
predict(x)

n-linear interpolation

new NLinearInterpolation()
Instance Members
fit(values, grids)
predict(x)

Nadaraya–Watson kernel regression

new NadarayaWatson(s: number?)
Parameters
s (number?) Sigmas of normal distribution
Instance Members
fit(x, y)
predict(x)

Naive bayes

new NaiveBayes(distribution: "gaussian")
Parameters
distribution ("gaussian" = gaussian) Distribution name
Instance Members
fit(datas, labels)
probability(data)
predict(data)

Narrow Adaptive Regularization Of Weights

new NAROW(b: number)
Parameters
b (number = 1) Tuning parameter
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Natural neighbor interpolation

new NaturalNeighborInterpolation()
Instance Members
fit(x, y)
predict(x)

NeighbourhoodComponentsAnalysis

lib/model/nca.js

Neighbourhood components analysis

new NeighbourhoodComponentsAnalysis(d: number?, lr: number)
Parameters
d (number? = null) Reduced dimension
lr (number = 0.1) Learning rate
Instance Members
fit(x, y)
importance()
predict(x)

Nearest centroid classifier

new NearestCentroid(metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))
Parameters
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point, category)
fit(datas, targets)
predict(datas)

Negation Naive bayes

new NegationNaiveBayes(distribution: "gaussian")
Parameters
distribution ("gaussian" = gaussian) Distribution name
Instance Members
fit(datas, labels)
predict(data)

Neural gas model

new NeuralGas(l: number, m: number)
Parameters
l (number = 1) Neughborhood range
m (number = 0.99) Decreasing factor of l
Instance Members
centroids
size
add(datas)
clear()
predict(datas)
fit(datas)

NeuralnetworkException

lib/model/neuralnetwork.js

Exception for neuralnetwork class

new NeuralnetworkException(message: string, value: any)

Extends Error

Parameters
message (string) Error message
value (any) Some value

Neuralnetwork

new NeuralNetwork(graph: ComputationalGraph, optimizer: ("sgd" | "adam" | "momentum" | "rmsprop"))
Parameters
graph (ComputationalGraph) Graph of a network
optimizer (("sgd" | "adam" | "momentum" | "rmsprop") = sgd) Optimizer of the network
Static Members
fromObject(layers, loss?, optimizer)
fromONNX(buffer)
Instance Members
copy()
toObject()
calc(x, t?, out?, options)
grad(e?)
update(learning_rate)
fit(x, t, epoch, learning_rate, batch_size = null, options = {})
predict(x)

NiblackThresholding

lib/model/niblack.js

Niblack thresholding

new NiblackThresholding(n: number, k: number)
Parameters
n (number = 3) Size of local range
k (number = 0.1) Tuning parameter
Instance Members
predict(x)

Flow-based generative model non-linear independent component estimation

new NICE(layer_number: number, optimizer: string)
Parameters
layer_number (number) Number of layers
optimizer (string) Optimizer of the network
Instance Members
epoch
fit(x, iteration, rate, batch_size)
predict(x)
generate(z)

Non-local means filter

new NLMeans(n: number, h: number)
Parameters
n (number) Manhattan distance of the pixel to the nearest neighbor
h (number) Degree of filtering
Instance Members
predict(x)

Non-negative matrix factorization

new NMF()
Instance Members
init(x, rd)
fit()
predict()

Natural Neighborhood Based Classification Algorithm

new NNBCA(metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))
Parameters
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
fit(datas, targets)
predict(datas)
Node

Type: object

Properties
layer (Layer) : Layer
name (string) : Name of the node
input (Array<string>?) : Input node names

ComputationalGraph

lib/model/nns/graph.js

Computational graph for Neuralnetwork structure

new ComputationalGraph()
Static Members
fromObject(nodes)
Instance Members
nodes
size
toObject()
add(layer, name?, inputs = undefined)
getNode(name)

Neuralnetwork layer

new Layer(obj: object)
Parameters
obj (object) Config
Static Members
fromObject(obj)
registLayer(name?, cls?)
Instance Members
bind(values)
calc(x)
grad(bo)
update(optimizer)
toObject()

Base class for loss layer

new LossLayer()

Extends Layer

Base class for Flow-based generative model

new FlowLayer()

Extends Layer

Instance Members
inverse(y)
jacobianDeterminant()

ONNX importer

new ONNXImporter()
Static Members
load(buffer)
jspb

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new AttributeProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
AttributeType
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getName()
setName(value)
clearName()
hasName()
getRefAttrName()
setRefAttrName(value)
clearRefAttrName()
hasRefAttrName()
getDocString()
setDocString(value)
clearDocString()
hasDocString()
getType()
setType(value)
clearType()
hasType()
getF()
setF(value)
clearF()
hasF()
getI()
setI(value)
clearI()
hasI()
getS()
getS_asB64()
getS_asU8()
setS(value)
clearS()
hasS()
getT()
setT(value)
clearT()
hasT()
getG()
setG(value)
clearG()
hasG()
getSparseTensor()
setSparseTensor(value)
clearSparseTensor()
hasSparseTensor()
getTp()
setTp(value)
clearTp()
hasTp()
getFloatsList()
setFloatsList(value)
addFloats(value, opt_index?)
clearFloatsList()
getIntsList()
setIntsList(value)
addInts(value, opt_index?)
clearIntsList()
getStringsList()
getStringsList_asB64()
getStringsList_asU8()
setStringsList(value)
addStrings(value, opt_index?)
clearStringsList()
getTensorsList()
setTensorsList(value)
addTensors(opt_value?, opt_index?)
clearTensorsList()
getGraphsList()
setGraphsList(value)
addGraphs(opt_value?, opt_index?)
clearGraphsList()
getSparseTensorsList()
setSparseTensorsList(value)
addSparseTensors(opt_value?, opt_index?)
clearSparseTensorsList()
getTypeProtosList()
setTypeProtosList(value)
addTypeProtos(opt_value?, opt_index?)
clearTypeProtosList()

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new ValueInfoProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getName()
setName(value)
clearName()
hasName()
getType()
setType(value)
clearType()
hasType()
getDocString()
setDocString(value)
clearDocString()
hasDocString()

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new NodeProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getInputList()
setInputList(value)
addInput(value, opt_index?)
clearInputList()
getOutputList()
setOutputList(value)
addOutput(value, opt_index?)
clearOutputList()
getName()
setName(value)
clearName()
hasName()
getOpType()
setOpType(value)
clearOpType()
hasOpType()
getDomain()
setDomain(value)
clearDomain()
hasDomain()
getAttributeList()
setAttributeList(value)
addAttribute(opt_value?, opt_index?)
clearAttributeList()
getDocString()
setDocString(value)
clearDocString()
hasDocString()

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new TrainingInfoProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getInitialization()
setInitialization(value)
clearInitialization()
hasInitialization()
getAlgorithm()
setAlgorithm(value)
clearAlgorithm()
hasAlgorithm()
getInitializationBindingList()
setInitializationBindingList(value)
addInitializationBinding(opt_value?, opt_index?)
clearInitializationBindingList()
getUpdateBindingList()
setUpdateBindingList(value)
addUpdateBinding(opt_value?, opt_index?)
clearUpdateBindingList()

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new ModelProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getIrVersion()
setIrVersion(value)
clearIrVersion()
hasIrVersion()
getOpsetImportList()
setOpsetImportList(value)
addOpsetImport(opt_value?, opt_index?)
clearOpsetImportList()
getProducerName()
setProducerName(value)
clearProducerName()
hasProducerName()
getProducerVersion()
setProducerVersion(value)
clearProducerVersion()
hasProducerVersion()
getDomain()
setDomain(value)
clearDomain()
hasDomain()
getModelVersion()
setModelVersion(value)
clearModelVersion()
hasModelVersion()
getDocString()
setDocString(value)
clearDocString()
hasDocString()
getGraph()
setGraph(value)
clearGraph()
hasGraph()
getMetadataPropsList()
setMetadataPropsList(value)
addMetadataProps(opt_value?, opt_index?)
clearMetadataPropsList()
getTrainingInfoList()
setTrainingInfoList(value)
addTrainingInfo(opt_value?, opt_index?)
clearTrainingInfoList()
getFunctionsList()
setFunctionsList(value)
addFunctions(opt_value?, opt_index?)
clearFunctionsList()

StringStringEntryProto

lib/model/nns/onnx/onnx_pb.js

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new StringStringEntryProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getKey()
setKey(value)
clearKey()
hasKey()
getValue()
setValue(value)
clearValue()
hasValue()

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new TensorAnnotation(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getTensorName()
setTensorName(value)
clearTensorName()
hasTensorName()
getQuantParameterTensorNamesList()
setQuantParameterTensorNamesList(value)
addQuantParameterTensorNames(opt_value?, opt_index?)
clearQuantParameterTensorNamesList()

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new GraphProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getNodeList()
setNodeList(value)
addNode(opt_value?, opt_index?)
clearNodeList()
getName()
setName(value)
clearName()
hasName()
getInitializerList()
setInitializerList(value)
addInitializer(opt_value?, opt_index?)
clearInitializerList()
getSparseInitializerList()
setSparseInitializerList(value)
addSparseInitializer(opt_value?, opt_index?)
clearSparseInitializerList()
getDocString()
setDocString(value)
clearDocString()
hasDocString()
getInputList()
setInputList(value)
addInput(opt_value?, opt_index?)
clearInputList()
getOutputList()
setOutputList(value)
addOutput(opt_value?, opt_index?)
clearOutputList()
getValueInfoList()
setValueInfoList(value)
addValueInfo(opt_value?, opt_index?)
clearValueInfoList()
getQuantizationAnnotationList()
setQuantizationAnnotationList(value)
addQuantizationAnnotation(opt_value?, opt_index?)
clearQuantizationAnnotationList()

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new TensorProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
new Segment(opt_data?)
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
DataLocation
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getDimsList()
setDimsList(value)
addDims(value, opt_index?)
clearDimsList()
getDataType()
setDataType(value)
clearDataType()
hasDataType()
getSegment()
setSegment(value)
clearSegment()
hasSegment()
getFloatDataList()
setFloatDataList(value)
addFloatData(value, opt_index?)
clearFloatDataList()
getInt32DataList()
setInt32DataList(value)
addInt32Data(value, opt_index?)
clearInt32DataList()
getStringDataList()
getStringDataList_asB64()
getStringDataList_asU8()
setStringDataList(value)
addStringData(value, opt_index?)
clearStringDataList()
getInt64DataList()
setInt64DataList(value)
addInt64Data(value, opt_index?)
clearInt64DataList()
getName()
setName(value)
clearName()
hasName()
getDocString()
setDocString(value)
clearDocString()
hasDocString()
getRawData()
getRawData_asB64()
getRawData_asU8()
setRawData(value)
clearRawData()
hasRawData()
getExternalDataList()
setExternalDataList(value)
addExternalData(opt_value?, opt_index?)
clearExternalDataList()
getDataLocation()
setDataLocation(value)
clearDataLocation()
hasDataLocation()
getDoubleDataList()
setDoubleDataList(value)
addDoubleData(value, opt_index?)
clearDoubleDataList()
getUint64DataList()
setUint64DataList(value)
addUint64Data(value, opt_index?)
clearUint64DataList()

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new SparseTensorProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getValues()
setValues(value)
clearValues()
hasValues()
getIndices()
setIndices(value)
clearIndices()
hasIndices()
getDimsList()
setDimsList(value)
addDims(value, opt_index?)
clearDimsList()

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new TensorShapeProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
new Dimension(opt_data?)
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getDimList()
setDimList(value)
addDim(opt_value?, opt_index?)
clearDimList()

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new TypeProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
new Tensor(opt_data?)
new Sequence(opt_data?)
new Map(opt_data?)
new Optional(opt_data?)
new SparseTensor(opt_data?)
ValueCase
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
getValueCase()
toObject(opt_includeInstance?)
serializeBinary()
getTensorType()
setTensorType(value)
clearTensorType()
hasTensorType()
getSequenceType()
setSequenceType(value)
clearSequenceType()
hasSequenceType()
getMapType()
setMapType(value)
clearMapType()
hasMapType()
getOptionalType()
setOptionalType(value)
clearOptionalType()
hasOptionalType()
getSparseTensorType()
setSparseTensorType(value)
clearSparseTensorType()
hasSparseTensorType()
getDenotation()
setDenotation(value)
clearDenotation()
hasDenotation()

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new OperatorSetIdProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getDomain()
setDomain(value)
clearDomain()
hasDomain()
getVersion()
setVersion(value)
clearVersion()
hasVersion()

Generated by JsPbCodeGenerator.

new FunctionProto(opt_data: Array?)

Extends jspb.Message

Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
displayName
toObject(includeInstance, msg)
deserializeBinary(bytes)
deserializeBinaryFromReader(msg, reader)
serializeBinaryToWriter(message, writer)
Instance Members
toObject(opt_includeInstance?)
serializeBinary()
getName()
setName(value)
clearName()
hasName()
getInputList()
setInputList(value)
addInput(value, opt_index?)
clearInputList()
getOutputList()
setOutputList(value)
addOutput(value, opt_index?)
clearOutputList()
getAttributeList()
setAttributeList(value)
addAttribute(value, opt_index?)
clearAttributeList()
getNodeList()
setNodeList(value)
addNode(opt_value?, opt_index?)
clearNodeList()
getDocString()
setDocString(value)
clearDocString()
hasDocString()
getOpsetImportList()
setOpsetImportList(value)
addOpsetImport(opt_value?, opt_index?)
clearOpsetImportList()
getDomain()
setDomain(value)
clearDomain()
hasDomain()
Version

Type: number

OperatorStatus

Type: number

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readString

Type: string

readEnum

Type: !proto.onnx.AttributeProto.AttributeType

readEnum

Type: !proto.onnx.TensorProto.DataLocation

readFloat

Type: number

readInt64

Type: number

readInt64

Type: number

readInt64

Type: number

readInt64

Type: number

readInt64

Type: number

readInt64

Type: number

readInt64

Type: number

readBytes

Type: !Uint8Array

readBytes

Type: !Uint8Array

readBytes

Type: !Uint8Array

readBytes

Type: !Uint8Array

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

isDelimited

Type: !Array<number>

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: !proto.onnx.AttributeProto.AttributeType

getField

Type: number

getField

Type: number

getField

Type: !(string | Uint8Array)

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: number

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: number

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: number

getField

Type: string

getField

Type: string

getField

Type: !(string | Uint8Array)

getField

Type: !proto.onnx.TensorProto.DataLocation

getField

Type: number

getField

Type: number

getField

Type: number

getField

Type: string

getField

Type: string

getField

Type: string

getField

Type: number

getField

Type: number

getField

Type: number

getField

Type: string

getField

Type: number

getField

Type: string

getField

Type: string

getField

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: !proto.onnx.AttributeProto.AttributeType

getFieldWithDefault

Type: number

getFieldWithDefault

Type: !(string | Uint8Array)

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: number

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: number

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: number

getFieldWithDefault

Type: number

getFieldWithDefault

Type: number

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: !(string | Uint8Array)

getFieldWithDefault

Type: !proto.onnx.TensorProto.DataLocation

getFieldWithDefault

Type: number

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: number

getFieldWithDefault

Type: number

getFieldWithDefault

Type: number

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: number

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFieldWithDefault

Type: string

getFloatingPointFieldWithDefault

lib/model/nns/onnx/onnx_pb.js
getFloatingPointFieldWithDefault

Type: number

bytesAsB64

Type: string

bytesAsB64

Type: string

bytesAsU8

Type: !Uint8Array

bytesAsU8

Type: !Uint8Array

getWrapperField

Type: proto.onnx.TensorProto?

getWrapperField

Type: proto.onnx.GraphProto?

getWrapperField

Type: proto.onnx.SparseTensorProto?

getWrapperField

Type: proto.onnx.TypeProto?

getWrapperField

Type: proto.onnx.TypeProto?

getWrapperField

Type: proto.onnx.GraphProto?

getWrapperField

Type: proto.onnx.GraphProto?

getWrapperField

Type: proto.onnx.GraphProto?

getWrapperField

Type: proto.onnx.TensorProto.Segment?

getWrapperField

Type: proto.onnx.TensorProto?

getWrapperField

Type: proto.onnx.TensorProto?

getWrapperField

Type: proto.onnx.TensorShapeProto?

getWrapperField

Type: proto.onnx.TypeProto?

getWrapperField

Type: proto.onnx.TypeProto?

getWrapperField

Type: proto.onnx.TypeProto?

getWrapperField

Type: proto.onnx.TensorShapeProto?

getWrapperField

Type: proto.onnx.TypeProto.Tensor?

getWrapperField

Type: proto.onnx.TypeProto.Sequence?

getWrapperField

Type: proto.onnx.TypeProto.Map?

getWrapperField

Type: proto.onnx.TypeProto.Optional?

getWrapperField

Type: proto.onnx.TypeProto.SparseTensor?

getRepeatedFloatingPointField

lib/model/nns/onnx/onnx_pb.js
getRepeatedFloatingPointField

Type: !Array<number>

getRepeatedFloatingPointField

lib/model/nns/onnx/onnx_pb.js
getRepeatedFloatingPointField

Type: !Array<number>

getRepeatedFloatingPointField

lib/model/nns/onnx/onnx_pb.js
getRepeatedFloatingPointField

Type: !Array<number>

getRepeatedField

Type: !Array<number>

getRepeatedField

Type: !(Array<!Uint8Array> | Array<string>)

getRepeatedField

Type: !Array<string>

getRepeatedField

Type: !Array<string>

getRepeatedField

Type: !Array<number>

getRepeatedField

Type: !Array<number>

getRepeatedField

Type: !(Array<!Uint8Array> | Array<string>)

getRepeatedField

Type: !Array<number>

getRepeatedField

Type: !Array<number>

getRepeatedField

Type: !Array<number>

getRepeatedField

Type: !Array<string>

getRepeatedField

Type: !Array<string>

getRepeatedField

Type: !Array<string>

bytesListAsB64

Type: !Array<string>

bytesListAsB64

Type: !Array<string>

bytesListAsU8

Type: !Array<!Uint8Array>

bytesListAsU8

Type: !Array<!Uint8Array>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.TensorProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.GraphProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.SparseTensorProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.TypeProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.AttributeProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.StringStringEntryProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.StringStringEntryProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.OperatorSetIdProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.StringStringEntryProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.TrainingInfoProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.FunctionProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.StringStringEntryProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.NodeProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.TensorProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.SparseTensorProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.ValueInfoProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.ValueInfoProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.ValueInfoProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.TensorAnnotation>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.StringStringEntryProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.TensorShapeProto.Dimension>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.NodeProto>

getRepeatedWrapperField

lib/model/nns/onnx/onnx_pb.js
getRepeatedWrapperField

Type: !Array<!proto.onnx.OperatorSetIdProto>

readInt32

Type: number

readInt32

Type: number

readInt32

Type: number

readInt32

Type: number

computeOneofCase

Type: proto.onnx.TensorShapeProto.Dimension.ValueCase

computeOneofCase

Type: proto.onnx.TypeProto.ValueCase

Handle abs operator

HandleONNXAbsOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#abs
Static Members
import(model, node)

Handle acos operator

HandleONNXAcosOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#acos
Static Members
import(model, node)

Handle acosh operator

HandleONNXAcoshOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#acosh
Static Members
import(model, node)

Handle add operator

HandleONNXAddOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#add
Static Members
import(model, node)

Handle asin operator

HandleONNXAsinOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#asin
Static Members
import(model, node)

Handle asinh operator

HandleONNXAsinhOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#asinh
Static Members
import(model, node)

Handle atan operator

HandleONNXAtanOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#atan
Static Members
import(model, node)

Handle atanh operator

HandleONNXAtanhOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#atanh
Static Members
import(model, node)

HandleONNXAveragePoolOperator

lib/model/nns/onnx/operators/averagepool.js

Handle averagepool operator

HandleONNXAveragePoolOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#AveragePool
Static Members
import(model, node)

Handle concat operator

HandleONNXConcatOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#concat
Static Members
import(model, node)

Handle constant operator

HandleONNXConstantOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#constant
Static Members
import(model, node)

Handle conv operator

HandleONNXConvOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#conv
Static Members
import(model, node)

Handle cos operator

HandleONNXCosOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#cos
Static Members
import(model, node)

Handle cosh operator

HandleONNXCoshOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#cosh
Static Members
import(model, node)

Handle div operator

HandleONNXDivOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#div
Static Members
import(model, node)

Handle dropout operator

HandleONNXDropoutOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#Dropout
Static Members
import(model, node)

Handle elu operator

HandleONNXEluOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#Elu
Static Members
import(model, node)

Handle exp operator

HandleONNXExpOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#exp
Static Members
import(model, node)

Handle gemm operator

HandleONNXGemmOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#gemm
Static Members
import(model, node)

Handle input node

HandleONNXInputNode
Static Members
import(model, node)

Handle leakyrelu operator

HandleONNXLeakyReluOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#LeakyRelu
Static Members
import(model, node)

Handle log operator

HandleONNXLogOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#log
Static Members
import(model, node)

Handle lrn operator

HandleONNXLRNOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#LRN
Static Members
import(model, node)

Handle matmul operator

HandleONNXMatmulOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#matmul
Static Members
import(model, node)

Handle maxpool operator

HandleONNXMaxPoolOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#maxpool
Static Members
import(model, node)

Handle mul operator

HandleONNXMulOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#mul
Static Members
import(model, node)

Handle neg operator

HandleONNXNegOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#neg
Static Members
import(model, node)

Handle output node

HandleONNXOutputNode
Static Members
import(model, node)

Handle prelu operator

HandleONNXPReluOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#PRelu
Static Members
import(model, node)

Handle relu operator

HandleONNXReluOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#relu
Static Members
import(model, node)

Handle reshape operator

HandleONNXReshapeOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#reshape
Static Members
import(model, node)

Handle sigmoid operator

HandleONNXSigmoidOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#sigmoid
Static Members
import(model, node)

Handle sin operator

HandleONNXSinOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#sin
Static Members
import(model, node)

Handle sinh operator

HandleONNXSinhOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#sinh
Static Members
import(model, node)

Handle softmax operator

HandleONNXSoftmaxOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#Softmax
Static Members
import(model, node)

Handle softplus operator

HandleONNXSoftplusOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#Softplus
Static Members
import(model, node)

Handle softsign operator

HandleONNXSoftsignOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#Softsign
Static Members
import(model, node)

Handle sqrt operator

HandleONNXSqrtOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#sqrt
Static Members
import(model, node)

Handle sub operator

HandleONNXSubOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#sub
Static Members
import(model, node)

Handle tan operator

HandleONNXTanOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#tan
Static Members
import(model, node)

Handle tanh operator

HandleONNXTanhOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#tanh
Static Members
import(model, node)

Handle transpose operator

HandleONNXTransposeOperator
Related
https://github.com/onnx/onnx/blob/main/docs/Operators.md#transpose
Static Members
import(model, node)

Return Tensor value.

loadTensor
Parameters
tensor (onnx.TensorProto.AsObject) TensorProto
Related
https://github.com/onnx/onnx/blob/main/onnx/onnx.proto#L479

Return attribute value.

loadAttribute
Parameters
attribute (onnx.AttributeProto.AsObject) AttributeProto
Returns
any: Attribute value
Related
https://github.com/onnx/onnx/blob/main/onnx/onnx.proto#L108
requireTensor
Parameters
model (onnx.ModelProto.AsObject) Model object
names (Array<string>) Input name
Returns
Array<object>: Require layer objects

Normal Herd

new NormalHERD(type: ("full" | "exact" | "project" | "drop"), c: number)
Parameters
type (("full" | "exact" | "project" | "drop") = exact) Method name
c (number = 0.1) Tradeoff value between passiveness and aggressiveness
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

One-class support vector machine

new OCSVM(nu: number, kernel: ("gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)
Parameters
nu (number) Nu
kernel (("gaussian" | "linear" | function (Array<number>, Array<number>): number)) Kernel name
kernelArgs (Array<any>? = []) Arguments for kernel
Instance Members
init(x)
fit()
predict(x)

Outlier Detection using Indegree Number

new ODIN(k: number, t: number)
Parameters
k (number = 5) Number of neighborhoods
t (number = 0) Indegree threshold
Instance Members
predict(data)

OnlineGradientDescent

lib/model/ogd.js

Online gradient descent

new OnlineGradientDescent(c: number, loss: "zero_one")
Parameters
c (number = 1) Tuning parameter
loss ("zero_one" = zero_one) Loss type name
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Ordering points to identify the clustering structure

new OPTICS(eps: number, minPts: number, metric: ("euclid" | "manhattan" | "chebyshev"))
Parameters
eps (number = Infinity) Radius to determine neighborhood
minPts (number = 5) Number of neighborhood with core distance
metric (("euclid" | "manhattan" | "chebyshev") = euclid) Metric name
Instance Members
fit(datas)
predict(threshold)

OtsusThresholding

lib/model/otsu.js

Otus's thresholding

new OtsusThresholding()
Instance Members
predict(x)

Partitioning Around Medoids

new PAM(k: number)
Parameters
k (number) Number of clusters
Instance Members
init(datas)
fit()
predict()

Particle filter

new ParticleFilter()
Instance Members
fit(z)

Passing-Bablok method

new PassingBablok()
Instance Members
fit(x, y)
predict(x)

Passive Aggressive

new PA(v: (0 | 1 | 2))
Parameters
v ((0 | 1 | 2) = 0) Version number
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Perceptron Algorithm with Uneven Margins

new PAUM(rate: number, tp: number, tm: number)
Parameters
rate (number) Learning rate
tp (number) Margin parameter for +1
tm (number) Margin parameter for -1
Instance Members
init(train_x, train_y)
fit()
predict(data)

Principal component analysis

new PCA()
Instance Members
fit(x)
predict(x, rd)

Dual Principal component analysis

new DualPCA()
Instance Members
fit(x)
predict(x, rd)

Kernel Principal component analysis

new KernelPCA(kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)
Parameters
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number)) Kernel name
kernelArgs (Array<any>? = []) Arguments for kernel
Instance Members
fit(x)
predict(x, rd)

Principal component analysis for anomaly detection

new AnomalyPCA()

Extends PCA

Instance Members
fit(x)
predict(x)

PossibilisticCMeans

lib/model/pcm.js

Possibilistic c-means

new PossibilisticCMeans(m: number)
Parameters
m (number = 2) Fuzziness factor
Instance Members
init(datas)
add()
fit()
predict()

Principal component regression

new PCR()
Instance Members
fit(x, y)
predict(x)

Primal Estimated sub-GrAdientSOlver for SVM

new Pegasos(rate: number)
Parameters
rate (number) Learning rate
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

PercentileAnormaly

lib/model/percentile.js

Percentile anomaly detection

new PercentileAnormaly(percentile: number, distribution: ("data" | "normal"))
Parameters
percentile (number) Percentile value
distribution (("data" | "normal") = data) Distribution name
Instance Members
fit(data)
predict(x)

Perceptron

new Perceptron(rate: number)
Parameters
rate (number) Learning rate
Instance Members
init(train_x, train_y)
fit()
predict(data)

AveragedPerceptron

lib/model/perceptron.js

Averaged perceptron

new AveragedPerceptron(rate: number)
Parameters
rate (number) Learning rate
Instance Members
init(train_x, train_y)
fit()
predict(data)

MulticlassPerceptron

lib/model/perceptron.js

Multiclass perceptron

new MulticlassPerceptron(rate: number)
Parameters
rate (number) Learning rate
Instance Members
init(train_x, train_y)
fit()
predict(data)

PhansalkarThresholding

lib/model/phansalkar.js

Phansalkar thresholding

new PhansalkarThresholding(n: number, k: number, r: number, p: number, q: number)
Parameters
n (number = 3) Size of local range
k (number = 0.25) Tuning parameter
r (number = 0.5) Tuning parameter
p (number = 2) Tuning parameter
q (number = 10) Tuning parameter
Instance Members
predict(x)

Partial least squares regression

new PLS(l: number)
Parameters
l (number) Limit on the number of latent factors
Instance Members
init(x, y)
fit()
predict(x)

Probabilistic latent semantic analysis

new PLSA(k: number)
Parameters
k (number = 2) Number of clusters
Instance Members
init(x)
fit()
predict()

PoissonRegression

lib/model/poisson.js

Poisson regression

new PoissonRegression(rate: number)
Parameters
rate (number) Learning rate
Instance Members
fit(x, y)
predict(x)

Policy gradient agent

new PGAgent(env: RLEnvironmentBase, resolution: number)
Parameters
env (RLEnvironmentBase) Environment
resolution (number = 20) Resolution
Instance Members
reset()
get_score()
get_action(state)
update(action, state, reward, done, learning_rate)

Polynomial histogram

new PolynomialHistogram(p: number, h: number)
Parameters
p (number = 2) Order
h (number = 0.1) Bin size
Instance Members
fit(x)
predict(x)

Polynomial interpolation

new PolynomialInterpolation()
Instance Members
fit(x, y)
predict(x)

ProjectionPursuit

lib/model/ppr.js

Projection pursuit regression

new ProjectionPursuit(r: number)
Parameters
r (number = 5) Number of functions
Instance Members
fit(x, y)
predict(x)

Prewitt edge detection

new Prewitt(th: number)
Parameters
th (number) Threshold
Instance Members
predict(x)

Priestley–Chao kernel estimator

new PriestleyChao(h: number)
Parameters
h (number) Smoothing parameter for the kernel
Instance Members
fit(x, y)
predict(x)

Principal curves

new PrincipalCurve()
Instance Members
fit(x)
predict()

Probabilistic Principal component analysis

new ProbabilisticPCA(method: ("analysis" | "em" | "bayes"), rd: number)
Parameters
method (("analysis" | "em" | "bayes") = analysis) Method name
rd (number) Reduced dimension
Instance Members
fit(x)
predict(x)

Probit

new Probit()
Instance Members
init(train_x, train_y)
fit()
predict(data)

MultinomialProbit

lib/model/probit.js

Multinomial probit

new MultinomialProbit()

Extends Probit

Instance Members
fit(x, y)
predict(data)

Projectron

new Projectron(eta: number, kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))
Parameters
eta (number = 0) Threshold
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
init(train_x, train_y)
fit()
predict(data)

Projectron++

new Projectronpp(eta: number, kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))
Parameters
eta (number = 0) Threshold
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
init(train_x, train_y)
fit()
predict(data)

P-tile thresholding

new PTile(p: number)
Parameters
p (number = 0.5) Percentile value
Instance Members
predict(x)

Base class for Q-table

new QTableBase(env: RLEnvironmentBase, resolution: number)
Parameters
env (RLEnvironmentBase) Environment
resolution (number = 20) Resolution
Instance Members
tensor
states
actions
resolution
toArray()
best_action(state)

Q-learning agent

new QAgent(env: RLEnvironmentBase, resolution: number)
Parameters
env (RLEnvironmentBase) Environment
resolution (number = 20) Resolution
Instance Members
get_score()
get_action(state, greedy_rate)
update(action, state, next_state, reward)

Quadratic discriminant analysis

new QuadraticDiscriminant()
Instance Members
fit(x, y)
predict(data)

Quantile regression

new QuantileRegression(tau: number)
Parameters
tau (number = 0.5) Quantile value
Instance Members
fit(x, y, learningRate)
predict(x)

Bsae class for radius neighbor models

new RadiusNeighborBase(r: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))
Parameters
r (number = 1) Radius to determine neighborhood
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
_add(point, category?)

radius neighbor

new RadiusNeighbor(r: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends RadiusNeighborBase

Parameters
r (number = 1) Radius to determine neighborhood
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point, category)
fit(datas, targets)
predict(datas)

RadiusNeighborRegression

lib/model/radius_neighbor.js

radius neighbor regression

new RadiusNeighborRegression(r: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends RadiusNeighborBase

Parameters
r (number = 1) Radius to determine neighborhood
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point, category)
fit(datas, targets)
predict(datas)

SemiSupervisedRadiusNeighbor

lib/model/radius_neighbor.js

Semi-supervised radius neighbor

new SemiSupervisedRadiusNeighbor(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"))

Extends RadiusNeighborBase

Parameters
k (number = 5) Radius to determine neighborhood
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
Instance Members
add(point, category)
fit(datas, targets)
predict()

Ramer-Douglas-Peucker algorithm

new RamerDouglasPeucker(e: number)
Parameters
e (number = 0.1) Threshold of distance
Instance Members
fit(x, y)
predict(x)

Bsae class for random forest models

new RandomForest(tree_num: number, sampling_rate: number, tree_class: DecisionTree, tree_class_args: Array<any>?)
Parameters
tree_num (number) Number of trees
sampling_rate (number = 0.8) Sampling rate
tree_class (DecisionTree) Tree class
tree_class_args (Array<any>? = null) Arguments for constructor of tree class
Instance Members
depth
init(datas, targets)
fit()
predict_prob(datas)

RandomForestClassifier

lib/model/random_forest.js

Random forest classifier

new RandomForestClassifier(tree_num: number, sampling_rate: number, method: ("ID3" | "CART"))

Extends RandomForest

Parameters
tree_num (number) Number of trees
sampling_rate (number = 0.8) Sampling rate
method (("ID3" | "CART") = CART) Method name
Instance Members
predict(datas)

RandomForestRegressor

lib/model/random_forest.js

Random forest regressor

new RandomForestRegressor(tree_num: number, sampling_rate: number)

Extends RandomForest

Parameters
tree_num (number) Number of trees
sampling_rate (number = 0.8) Sampling rate
Instance Members
predict(datas)

Random projection

new RandomProjection(init: ("uniform" | "root3" | "normal"))
Parameters
init (("uniform" | "root3" | "normal") = uniform) Initialize method name
Instance Members
predict(x, rd)

RANSACSubModel

lib/model/ransac.js
RANSACSubModel

Type: object

Properties
fit (function (Array<Array<number>>, Array<any>): void) : Fit model
predict (function (Array<Array<number>>): Array<any>) : Returns predicted values
score (function (Array<any>, Array<any>): number?) : Returns a number how accurate the prediction is

Random sample consensus

new RANSAC(model: any, sample: (number | null))
Parameters
model (any)
sample ((number | null) = null) Sampling rate
Instance Members
fit(x, y)
predict(x)

RadialBasisFunctionNetwork

lib/model/rbf.js

Radial basis function network

new RadialBasisFunctionNetwork(rbf: ("linear" | "gaussian" | "multiquadric" | "inverse quadratic" | "inverse multiquadric" | "thin plate" | "bump"), e: number, l: number)
Parameters
rbf (("linear" | "gaussian" | "multiquadric" | "inverse quadratic" | "inverse multiquadric" | "thin plate" | "bump") = linear) RBF name
e (number = 1) Tuning parameter
l (number = 0) Regularization parameter
Instance Members
fit(x, y)
predict(target)

Restricted Boltzmann machine

new RBM(hiddenSize: number, lr: number)
Parameters
hiddenSize (number) Size of hidden layer
lr (number = 0.01) Learning rate
Instance Members
fit(x)
energy(v, h)
predict(x)

Gaussian-Bernouili Restricted Boltzmann machine

new GBRBM(hiddenSize: number, lr: number, fixSigma: boolean)
Parameters
hiddenSize (number) Size of hidden layer
lr (number = 0.01) Learning rate
fixSigma (boolean = false) Do not learn sigma or not
Instance Members
fit(x)
energy(v, h)
predict(x)

Randomized Budget Perceptron

new RBP(b: number)
Parameters
b (number) Number of support vectors
Instance Members
init(train_x, train_y)
fit()
predict(data)

Relative Density-based Outlier Score

new RDOS(k: number, h: number, kernel: ("gaussian" | function (Array<number>): number))
Parameters
k (number) Number of neighborhoods
h (number) Kernel width
kernel (("gaussian" | function (Array<number>): number) = gaussian) Kernel name
Instance Members
predict(datas)

Ridge regressioin

new Ridge(lambda: number)
Parameters
lambda (number = 0.1) Regularization strength
Instance Members
fit(x, y)
predict(x)
importance()

Kernel ridge regression

new KernelRidge(lambda: number, kernel: ("gaussian" | function (Array<number>, Array<number>): number))
Parameters
lambda (number = 0.1) Regularization strength
kernel (("gaussian" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
fit(x, y)
predict(x)
importance()

Robust Kernel-based Outlier Factor

new RKOF(k: number, h: number, alpha: number, kernel: ("gaussian" | "epanechnikov" | "volcano" | function (Array<number>): number))
Parameters
k (number) Number of neighborhoods
h (number) Smoothing parameter
alpha (number) Sensitivity parameter
kernel (("gaussian" | "epanechnikov" | "volcano" | function (Array<number>): number) = gaussian) Kernel name
Instance Members
predict(datas)

RecursiveLeastSquares

lib/model/rls.js

Recursive least squares

new RecursiveLeastSquares()
Instance Members
update(x, y)
fit(x, y)
predict(data)

RepeatedMedianRegression

lib/model/rmr.js

Repeated median regression

new RepeatedMedianRegression()
Instance Members
fit(x, y)
predict(x)

Recurrent neuralnetwork

new RNN(method: ("rnn" | "lstm" | "gru"), window: number, unit: number, out_size: number, optimizer: string)
Parameters
method (("rnn" | "lstm" | "gru") = lstm) Method name
window (number = 10) Window size
unit (number = 10) Size of recurrent unit
out_size (number = 1) Output size
optimizer (string = adam) Optimizer of the network
Instance Members
method
epoch
fit(train_x, train_y, iteration, rate, batch)
predict(data, k)

Roberts cross

new RobertsCross(th: number)
Parameters
th (number) Threshold
Instance Members
predict(x)

Robust scaler

new RobustScaler()
Instance Members
fit(x)
predict(x)
ROCKNode

Type: object

Properties
point (Array<number>?) : Data point of leaf node
index (number?) : Data index of leaf node
g (number) : Number of leaf nodes
distance (number?) : Distance between children nodes
children (Array<ROCKNode>?) : Children nodes
leafs (Array<ROCKNode>) : Leaf nodes

RObust Clustering using linKs

new ROCK(th: number)
Parameters
th (number) Threshold
Instance Members
fit(data)
getClusters(number)
predict(k)

Relaxed Online Maximum Margin Algorithm

new ROMMA()
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

AggressiveROMMA

lib/model/romma.js

Aggressive Relaxed Online Maximum Margin Algorithm

new AggressiveROMMA()

Extends ROMMA

Relevance vector machine

new RVM()
Instance Members
fit(x, y)
predict(x)

Semi-Supervised Support Vector Machine

new S3VM(kernel: ("gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)
Parameters
kernel (("gaussian" | "linear" | function (Array<number>, Array<number>): number)) Kernel name
kernelArgs (Array<any>? = []) Arguments for kernel
Instance Members
init(x, y)
fit()
predict(data)

Sammon mapping

new Sammon(x: Array<Array<number>>, rd: number)
Parameters
x (Array<Array<number>>) Sample data
rd (number) Reduced dimension
Instance Members
fit()
predict()

SARSA agent

new SARSAAgent(env: RLEnvironmentBase, resolution: number)
Parameters
env (RLEnvironmentBase) Environment
resolution (number = 20) Resolution
Instance Members
reset()
get_score()
get_action(state, greedy_rate)
update(action, state, next_state, reward)

SauvolaThresholding

lib/model/sauvola.js

sauvola thresholding

new SauvolaThresholding(n: number, k: number, r: number)
Parameters
n (number = 3) Size of local range
k (number = 0.1) Tuning parameter
r (number = 1) Tuning parameter
Instance Members
predict(x)

SavitzkyGolayFilter

lib/model/savitzky_golay.js

Savitzky-Golay filter

new SavitzkyGolayFilter(k: number)
Parameters
k (number) Number of coefficients
Instance Members
predict(x)

Sequentially Discounting Autoregressive model

new SDAR(p: number, r: number)
Deprecated: Does not work properly
Parameters
p (number = 1) Order
r (number = 0.8) Forgetting factor
Instance Members
probability(data)
predict(data, k)

SegmentedRegression

lib/model/segmented.js

Segmented regression

new SegmentedRegression(seg: number)
Parameters
seg (number = 3) Number of segments
Instance Members
fit(x, y)
predict(x)

Selective Naive bayes

new SelectiveNaiveBayes(distribution: "gaussian")
Parameters
distribution ("gaussian" = gaussian) Distribution name
Instance Members
fit(datas, labels)
predict(data)

Selective sampling Perceptron

new SelectiveSamplingPerceptron(b: number, rate: number)
Parameters
b (number) Smooth parameter
rate (number) Learning rate
Instance Members
init(train_x, train_y)
predict(data)

SelectiveSamplingAdaptivePerceptron

lib/model/selective_sampling_perceptron.js

Selective sampling Perceptron with adaptive parameter

new SelectiveSamplingAdaptivePerceptron(beta: number, rate: number)
Parameters
beta (number) Smooth parameter
rate (number) Learning rate
Instance Members
init(train_x, train_y)
predict(data)

Selective sampling second-order Perceptron

new SelectiveSamplingSOP(b: number)
Parameters
b (number) Smooth parameter
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

Selective sampling Winnow

new SelectiveSamplingWinnow(b: number, alpha: boolean)
Parameters
b (number) Smooth parameter
alpha (boolean = 2) Learning rate
Instance Members
init(train_x, train_y)
predict(data)

Self-training

new SelfTraining(model: object, threshold: number)
Parameters
model (object) View
threshold (number) Threshold
Instance Members
init(x, y)
fit()
predict()

Semi-supervised naive bayes

new SemiSupervisedNaiveBayes(lambda: number)
Parameters
lambda (number = 1) Weight applied to the contribution of the unlabeled data
Instance Members
init(datas, labels)
probability(datas)
logLikelihood()
predict(datas)

SezanThresholding

lib/model/sezan.js

Sezan's thresholding

new SezanThresholding(gamma: number, sigma: number)
Parameters
gamma (number = 0.5) Tradeoff value between black and white
sigma (number = 5) Sigma of normal distribution
Instance Members
predict(x)

Shifting Perceptron Algorithm

new ShiftingPerceptron(lambda: number)
Parameters
lambda (number) Rate of weight decay
Instance Members
init(train_x, train_y)
fit()
predict(data)

Sinc interpolation

new SincInterpolation()
Instance Members
fit(values)
predict(index)

SlicedInverseRegression

lib/model/sir.js

Sliced inverse regression

new SlicedInverseRegression(s: number)
Parameters
s (number) Number of slices
Instance Members
predict(x, y, rd)

Spherical linear interpolation

new Slerp(o: number)
Parameters
o (number = 1) Angle subtended by the arc
Instance Members
fit(x, y)
predict(target)

slice sampling

new SliceSampling(targetFunc: function (Array<number>): number, d: number, w: number)
Parameters
targetFunc (function (Array<number>): number) Target distribution
d (number) Output size
w (number = 1.0) Check width
Instance Members
sample(n)

SMARegression

lib/model/sma.js

Standardizes Major Axis regression

new SMARegression()
Instance Members
fit(x, y)
predict(x)

SmirnovGrubbs test

new SmirnovGrubbs(alpha: number)
Parameters
alpha (number) Significance level
Instance Members
predict(data)

SmoothstepInterpolation

lib/model/smoothstep.js

Smoothstep interpolation

new SmoothstepInterpolation(n: number)
Parameters
n (number = 1) Order
Instance Members
fit(x, y)
predict(target)

Snakes (active contour model)

new Snakes(alpha: number, beta: number, gamma: number, k: number)
Parameters
alpha (number) Penalty for length
beta (number) Penalty for curvature
gamma (number) Penalty for conformity with image
k (number = 100) Number of vertices
Instance Members
init(x)
fit()
predict()

Sobel edge detection

new Sobel(th: number)
Parameters
th (number) Threshold
Instance Members
predict(x)

Soft k-means

new SoftKMeans(beta: number)
Parameters
beta (number = 1) Tuning parameter
Instance Members
init(datas)
add()
fit()
predict()

Self-Organizing Map

new SOM(input_size: number, output_size: number, resolution: number)
Parameters
input_size (number) Input size
output_size (number) Output size
resolution (number = 20) Resolution of output
Instance Members
fit(data)
predict(x)

SecondOrderPerceptron

lib/model/sop.js

Second order perceptron

new SecondOrderPerceptron(a: number)
Parameters
a (number = 1) Tuning parameter
Instance Members
init(train_x, train_y)
update(x, y)
fit()
predict(data)

SpectralClustering

lib/model/spectral.js

Spectral clustering

new SpectralClustering(affinity: ("rbf" | "knn"), param: object)
Parameters
affinity (("rbf" | "knn") = rbf) Affinity type name
param (object = {}) Config
Instance Members
size
epoch
init(datas)
add()
clear()
predict()
fit()

Spline interpolation

new SplineInterpolation()
Instance Members
fit(x, y)
predict(target)

SmoothingSpline

lib/model/spline.js

Spline smoothing

new SmoothingSpline(l: number)
Parameters
l (number) Smoothing parameter
Instance Members
fit(x, y)
predict(data)

Split and merge segmentation

new SplitAndMerge(method: ("variance" | "uniformity"), threshold: number)
Parameters
method (("variance" | "uniformity") = variance) Method name
threshold (number = 0.1) Threshold
Instance Members
predict(x)

Squared-loss Mutual information change point detection

new SquaredLossMICPD(model: object, w: number, take: number?, lag: number?)
Parameters
model (object) Density ratio estimation model
w (number) Window size
take (number?) Take number
lag (number?) Lag
Instance Members
predict(datas)

Singular-spectrum transformation

new SST(w: number, take: number?, lag: number?)
Parameters
w (number) Window size
take (number?) Take number
lag (number?) Lag
Instance Members
predict(datas)

Standardization

new Standardization(ddof: number)
Parameters
ddof (number = 0) Delta Degrees of Freedom
Instance Members
fit(x)
predict(x)

Statistical Region Merging

new StatisticalRegionMerging(t: number)
Parameters
t (number) Threshold
Instance Members
predict(x)

STatistical INformation Grid-based method

new STING()
Deprecated: Not implemented
Instance Members
fit(datas)
predict(datas)

Stoptron

new Stoptron(n: number, kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number))
Parameters
n (number = 10) Cachs size
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
Instance Members
init(train_x, train_y)
fit()
predict(data)

Support vector clustering

new SVC(kernel: ("gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)
Parameters
kernel (("gaussian" | "linear" | function (Array<number>, Array<number>): number)) Kernel name
kernelArgs (Array<any>? = []) Arguments for kernel
Instance Members
size
init(x)
fit()
predict()

Support vector machine

new SVM(kernel: ("gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)
Parameters
kernel (("gaussian" | "linear" | function (Array<number>, Array<number>): number)) Kernel name
kernelArgs (Array<any>? = []) Arguments for kernel
Instance Members
init(train_x, train_y)
fit()
predict(data)

Support vector regression

new SVR(kernel: ("gaussian" | "linear" | function (Array<number>, Array<number>): number), kernelArgs: Array<any>?)
Parameters
kernel (("gaussian" | "linear" | function (Array<number>, Array<number>): number)) Kernel name
kernelArgs (Array<any>? = []) Arguments for kernel
Instance Members
init(x, y)
fit()
predict(x)

TheilSenRegression

lib/model/theil_sen.js

Theil-Sen regression

new TheilSenRegression()
Instance Members
fit(x, y)
predict(x)

Thompson test

new Thompson(alpha: number)
Parameters
alpha (number) Significance level
Instance Members
predict(data)

Tietjen-Moore Test

new TietjenMoore(k: number)
Parameters
k (number) Number of outliers
Instance Members
predict(data, threshold)

Tighter Budget Perceptron

new TighterPerceptron(beta: number, p: number, update: ("perceptron" | "mira" | "nobias"))
Parameters
beta (number = 0) Margine
p (number = 0) Cachs size
update (("perceptron" | "mira" | "nobias") = perceptron) Update rule
Instance Members
init(train_x, train_y)
fit()
predict(data)

Tightest Perceptron

new TightestPerceptron(b: number, kernel: ("gaussian" | "polynomial" | function (Array<number>, Array<number>): number), accuracyLoss: ("zero_one" | "hinge"))
Parameters
b (number = 10) Budget size
kernel (("gaussian" | "polynomial" | function (Array<number>, Array<number>): number) = gaussian) Kernel name
accuracyLoss (("zero_one" | "hinge") = hinge) Accuracy loss type name
Instance Members
init(train_x, train_y)
fit()
predict(data)

Trigonometric interpolation

new TrigonometricInterpolation()
Instance Members
fit(x, y)
predict(target)

Stochastic Neighbor Embedding

new SNE(datas: Array<Array<number>>, rd: number, perplexity: number)
Parameters
datas (Array<Array<number>>) Training data
rd (number = 1) Reduced dimension
perplexity (number = 30) Perplexity
Instance Members
fit()
predict()

T-distributed Stochastic Neighbor Embedding

new tSNE(datas: Array<Array<number>>, rd: number, perplexity: number)
Parameters
datas (Array<Array<number>>) Training data
rd (number = 1) Reduced dimension
perplexity (number = 30) Perplexity
Instance Members
fit()
predict()

Tukey regression

new TukeyRegression(e: number)
Parameters
e (number) Error tolerance
Instance Members
fit(x, y)
predict(x)

Tukey's fences

new TukeysFences(k: number)
Parameters
k (number) Tuning parameter
Instance Members
predict(data)

Relative unconstrained Least-Squares Importance Fitting

new RuLSIF(sigma: Array<number>, lambda: Array<number>, alpha: number, kernelNum: number)
Parameters
sigma (Array<number>) Sigmas of normal distribution
lambda (Array<number>) Regularization parameters
alpha (number) Relative parameter
kernelNum (number) Number of kernels
Instance Members
fit(x1, x2)
predict(x)

unconstrained Least-Squares Importance Fitting

new uLSIF(sigma: Array<number>, lambda: Array<number>, kernelNum: number)

Extends RuLSIF

Parameters
sigma (Array<number>) Sigma of normal distribution
lambda (Array<number>) Regularization parameters
kernelNum (number) Number of kernels

Uniform Manifold Approximation and Projection

new UMAP(datas: Array<Array<number>>, rd: number, n: number, min_dist: number)
Parameters
datas (Array<Array<number>>) Training data
rd (number) Reduced dimension
n (number = 10) Number of neighborhoods
min_dist (number = 0.1) Minimum distance
Instance Members
fit()
predict()

Universal-set Naive bayes

new UniversalSetNaiveBayes(distribution: "gaussian")
Parameters
distribution ("gaussian" = gaussian) Distribution name
Instance Members
fit(datas, labels)
predict(data)

Variational Autoencoder

new VAE(in_size: number, noise_dim: number, enc_layers: Array<Object<string, any>>, dec_layers: Array<Object<string, any>>, optimizer: string, class_size: (number | null), type: ("" | "conditional"))
Parameters
in_size (number) Input size
noise_dim (number) Number of noise dimension
enc_layers (Array<Object<string, any>>) Layers of encoder
dec_layers (Array<Object<string, any>>) Layers of decoder
optimizer (string) Optimizer of the network
class_size ((number | null)) Class size for conditional type
type (("" | "conditional")) Type name
Instance Members
epoch
fit(x, y, iteration, rate, batch)
predict(x, y)
reduce(x, y)

Vector Autoregressive model

new VAR(p: number)
Parameters
p (number) Order
Instance Members
fit(data)
predict(data, k)

Variational Gaussian Mixture Model

new VBGMM(a: number, b: number, k: number)
Parameters
a (number) Tuning parameter
b (number) Tuning parameter
k (number) Initial number of clusters
Instance Members
means
covs
effectivity
init(datas)
fit()
probability(data)
predict(data)

Voted-perceptron

new VotedPerceptron(rate: number)
Parameters
rate (number = 1) Learning rate
Instance Members
init(train_x, train_y)
fit()
predict(data)

Weighted k-means model

new WeightedKMeans(beta: number)
Parameters
beta (number) Tuning parameter
Instance Members
centroids
size
add(datas)
clear()
predict(datas)
fit(datas)

Weighted K-Nearest Neighbor

new WeightedKNN(k: number, metric: ("euclid" | "manhattan" | "chebyshev" | "minkowski"), weight: ("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "quartic" | "triweight" | "cosine" | "inversion"))
Parameters
k (number) Number of neighbors
metric (("euclid" | "manhattan" | "chebyshev" | "minkowski") = euclid) Metric name
weight (("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "quartic" | "triweight" | "cosine" | "inversion") = gaussian) Weighting scheme name
Instance Members
fit(x, y)
predict(data)

Weighted least squares

new WeightedLeastSquares()
Instance Members
fit(x, y, w)
predict(x)

Winnow

new Winnow(alpha: boolean, threshold: number?, version: (1 | 2))
Parameters
alpha (boolean = 2) Learning rate
threshold (number? = null) Threshold
version ((1 | 2) = 1) Version of model
Instance Members
init(train_x, train_y)
fit()
predict(data)

Word2Vec

new Word2Vec(method: ("CBOW" | "skip-gram"), n: number, wordsOrNumber: (number | Array<string>), reduce_size: number, optimizer: string)
Parameters
method (("CBOW" | "skip-gram")) Method name
n (number) Number of how many adjacent words to learn
wordsOrNumber ((number | Array<string>)) Initial words or number of words
reduce_size (number) Reduced dimension
optimizer (string) Optimizer of the network
Instance Members
epoch
fit(words, iteration, rate, batch)
predict(x)
reduce(x)

eXtreme Gradient Boosting regression

new XGBoost(maxdepth: number, srate: number, lambda: number, lr: number)
Parameters
maxdepth (number = 1) Maximum depth of tree
srate (number = 1.0) Sampling rate
lambda (number = 0.1) Regularization parameter
lr (number = 0.5) Learning rate
Instance Members
size
init(x, y)
fit()
predict(x)

XGBoostClassifier

lib/model/xgboost.js

eXtreme Gradient Boosting classifier

new XGBoostClassifier(maxdepth: number, srate: number, lambda: number, lr: number)

Extends XGBoost

Parameters
maxdepth (number = 1) Maximum depth of tree
srate (number = 1.0) Sampling rate
lambda (number = 0.1) Regularization parameter
lr (number = 0) Learning rate
Instance Members
init(x, y)
predict(x)

x-means

new XMeans()
Instance Members
centroids
size
clear()
fit(datas, iterations)
predict(datas)

Yeo-Johnson power transformation

new YeoJohnson(lambda: number?)
Parameters
lambda (number? = null) Lambda
Instance Members
fit(x)
predict(x)

ZeroInflatedPoisson

lib/model/zip.js

Zero-inflated poisson

new ZeroInflatedPoisson()
Instance Members
fit(x)
probability(x)

AcrobotRLEnvironment

lib/rl/acrobot.js

Acrobot environment

new AcrobotRLEnvironment()

Extends RLEnvironmentBase

RLRealRange

lib/rl/base.js

Real number range state/actioin

new RLRealRange(min: number, max: number, space: ("equal" | "log"))
Parameters
min (number) Minimum value
max (number) Maximum value
space (("equal" | "log") = 'equal') Space type
Instance Members
toSpace(resolution)
toArray(resolution)
indexOf(value, resolution)

RLIntRange

lib/rl/base.js

Integer number range state/actioin

new RLIntRange(min: number, max: number)
Parameters
min (number) Minimum value
max (number) Maximum value
Instance Members
length
toArray(resolution)
indexOf(value, resolution)

RLEnvironmentBase

lib/rl/base.js

Base class for reinforcement learning environment

new RLEnvironmentBase()
Properties
actions (Array<(Array<any> | RLRealRange | RLIntRange)>) : Action variables
states (Array<(Array<any> | RLRealRange | RLIntRange)>) : States variables
Instance Members
epoch
reward
close()
reset()
state(agent)
setState(state, agent)
step(action, agent)
test(state, action, agent)
sample_action(agent)

EmptyRLEnvironment

lib/rl/base.js

Empty environment

new EmptyRLEnvironment()

Extends RLEnvironmentBase

BreakerRLEnvironment

lib/rl/breaker.js

Breaker environment

new BreakerRLEnvironment()

Extends RLEnvironmentBase

CartPoleRLEnvironment

lib/rl/cartpole.js

Cartpole environment

new CartPoleRLEnvironment()

Extends RLEnvironmentBase

DraughtsRLEnvironment

lib/rl/draughts.js

Draughts environment

new DraughtsRLEnvironment()

Extends RLEnvironmentBase

GomokuRLEnvironment

lib/rl/gomoku.js

Gomoku environment

new GomokuRLEnvironment()

Extends RLEnvironmentBase

GridMazeRLEnvironment

lib/rl/grid.js

Grid world environment

new GridMazeRLEnvironment()

Extends RLEnvironmentBase

InHypercubeRLEnvironment

lib/rl/inhypercube.js

In-hypercube environment

new InHypercubeRLEnvironment(d: number)

Extends RLEnvironmentBase

Parameters
d (number = 2) Dimension of the environment

SmoothMazeRLEnvironment

lib/rl/maze.js

Smooth maze environment

new SmoothMazeRLEnvironment(width: number, height: number)

Extends RLEnvironmentBase

Parameters
width (number) Area width
height (number) Area height

MountainCarRLEnvironment

lib/rl/mountaincar.js

MountainCar environment

new MountainCarRLEnvironment()

Extends RLEnvironmentBase

PendulumRLEnvironment

lib/rl/pendulum.js

Pendulum environment

new PendulumRLEnvironment()

Extends RLEnvironmentBase

ReversiRLEnvironment

lib/rl/reversi.js

Reversi environment

new ReversiRLEnvironment()

Extends RLEnvironmentBase

WaterballRLEnvironment

lib/rl/waterball.js

Waterball environment

new WaterballRLEnvironment(width: number, height: number)

Extends RLEnvironmentBase

Parameters
width (number) Area width
height (number) Area height

Complex number

new Complex(real: number, imag: number)
Parameters
real (number = 0) Real number
imag (number = 0) Imaginary number
Instance Members
real
imaginary
abs()
conjugate()
add(other)
sub(other)
mult(other)
div(other)
sqrt()
cbrt()

GraphException

lib/util/graph.js

Exception for graph class

new GraphException(message: string, value: any)

Extends Error

Parameters
message (string) Error message
value (any) Some value
constructor(from: number, to: number, value: unknown, direct: boolean)
Parameters
from (number) Index of the starting node of the edge
to (number) Index of the end node of the edge
value (unknown = null) Value of the edge
direct (boolean = false) true if the edge is direct

Graph class

new Graph(nodes: (number | Array<unknown>), edges: Array<([number, number] | Edge)>)
Parameters
nodes ((number | Array<unknown>) = 0) Number of nodes or values of nodes
edges (Array<([number, number] | Edge)> = []) Edges
Static Members
fromAdjacency(mat)
complete(n)
completeBipartite(n, m)
cycle(n, direct)
wheel(n)
Instance Members
order
size
nodes
edges
toDot()
toString()
copy()
degree(k, undirect, direct)
adjacencies(k, undirect, direct)
components()
diameter()
eccentricity(k)
radius()
center()
girth()
clique(k?)
addNode(value?)
getNode(k?)
removeNode(k)
addEdge(from, to, value, direct)
getEdges(from, to, direct)
removeEdges(from, to, direct)
adjacencyMatrix()
degreeMatrix(direct)
laplacianMatrix()
isNull()
isEdgeless()
isUndirected()
isDirected()
isMixed()
isWeighted()
isSimple()
isConnected()
isBiconnected()
isTree()
isForest()
isBipartite()
isRegular()
isPlainer()
isSymmetric()
isDAG()
hasCycle()
hasCycleDFS()
hasCycleEachNodes()
isomorphism(g)
inducedSub(k)
complement()
contraction(a, b)
subdivision(a, b)
disjointUnion(g)
substitution(k, g)
cartesianProduct(g)
tensorProduct(g)
strongProduct(g)
lexicographicProduct(g)
shortestPath(from?)
shortestPathBreadthFirstSearch(from)
shortestPathDijkstra(from)
shortestPathBellmanFord(from)
shortestPathFloydWarshall()
minimumSpanningTree()
minimumSpanningTreePrim()
minimumSpanningTreeKruskal()
minimumSpanningTreeBoruvka()
cut(s, t)
mincut(minv)
mincutBruteForce(minv)
mincutStoerWagner(minv, startnode)
mincutKargers(minv, trials = null)
mincutKargersStein(minv, trials = null)
bisectionSpectral()

MatrixException

lib/util/matrix.js

Exception for matrix class

new MatrixException(message: string, value: any)

Extends Error

Parameters
message (string) Error message
value (any) Some value

Matrix class

new Matrix(rows: number, cols: number, values: (number | Array<number> | Array<Array<number>>)?)
Parameters
rows (number) Number of rows
cols (number) Number of columns
values ((number | Array<number> | Array<Array<number>>)?) Initial values
Static Members
zeros(rows, cols)
ones(rows, cols)
eye(rows, cols, init)
random(rows, cols, min, max)
randint(rows, cols, min, max)
randn(rows, cols, myu, sigma)
diag(d)
fromArray(arr)
map(mat, cb)
resize(mat, rows, cols, init)
repeat(mat, n, axis)
concat(a, b, axis)
add(a, b)
sub(a, b)
mult(a, b)
div(a, b)
mod(a, b)
and(a, b)
or(a, b)
bitand(a, b)
bitor(a, b)
bitxor(a, b)
Instance Members
dimension
sizes
length
rows
cols
value
t
iterator()
toArray()
toScaler()
toString()
copy(dst?)
equals(other, tol)
at(r, c?)
set(r, c, value?)
row(r)
col(c)
slice(from, to, axis)
block(rows_from, cols_from, rows_to?, cols_to?)
remove(idx, axis)
removeIf(cond, axis)
sample(n, axis)
fill(value)
map(cb)
forEach(cb)
transpose()
adjoint()
flip(axis)
swap(a, b, axis)
sort(axis)
shuffle(axis)
unique(axis, tol)
resize(rows, cols, init)
reshape(rows, cols)
repeat(n, axis)
concat(m, axis)
reduce(cb, init?, axis, keepdims)
every(cb, axis)
some(cb, axis)
max(axis)
min(axis)
median(axis)
quantile(q, axis)
argmax(axis)
argmin(axis)
sum(axis)
mean(axis)
prod(axis)
variance(axis, ddof)
std(axis, ddof)
isSquare()
isDiag(tol)
isIdentity(tol)
isZero(tol)
isTriangular(tol)
isLowerTriangular(tol)
isUpperTriangular(tol)
isSymmetric(tol)
isHermitian(tol)
isAlternating(tol)
isSkewHermitian(tol)
isRegular(tol)
isNormal(tol)
isOrthogonal(tol)
isUnitary(tol)
isNilpotent(tol)
diag()
trace()
norm(p)
normInduced(p)
normSpectral()
normEntrywise(p)
normFrobenius()
normMax()
normSchatten(p)
normNuclear()
rank(tol)
det()
negative()
not()
bitnot()
abs()
round()
floor()
ceil()
leftShift(n)
signedRightShift(n)
unsignedRightShift(n)
broadcastOperate(o, fn)
operateAt(r, c, fn?)
add(o)
addAt(r, c, v)
sub(o)
isub(o)
subAt(r, c, v)
isubAt(r, c, v)
mult(o)
multAt(r, c, v)
div(o)
idiv(o)
divAt(r, c, v)
idivAt(r, c, v)
mod(o)
imod(o)
modAt(r, c, v)
imodAt(r, c, v)
and(o)
andAt(r, c, v)
or(o)
orAt(r, c, v)
bitand(o)
bitandAt(r, c, v)
bitor(o)
bitorAt(r, c, v)
bitxor(o)
bitxorAt(r, c, v)
dot(o)
tDot(o)
kron(mat)
convolute(kernel, normalize)
reducedRowEchelonForm(tol)
inv()
invLowerTriangular()
invUpperTriangular()
invRowReduction()
invLU()
sqrt()
power(p)
exp()
log()
cov(ddof)
gram()
solve(b)
solveLowerTriangular(b)
solveUpperTriangular(b)
bidiag()
tridiag()
tridiagHouseholder()
tridiagLanczos(k)
hessenberg()
hessenbergArnoldi(k)
lu()
qr()
qrGramSchmidt()
qrHouseholder()
singularValues()
svd()
svdEigen()
cholesky()
choleskyBanachiewicz()
choleskyLDL()
schur()
eigen()
eigenValues()
eigenVectors()
eigenValuesBiSection()
eigenValuesLR()
eigenValuesQR()
eigenJacobi(maxIteration)
eigenPowerIteration()
eigenInverseIteration(ev)

Tensor class

new Tensor(size: Array<number>, value: (number | Array<number>)?)
Parameters
size (Array<number>) Sizes for each dimension
value ((number | Array<number>)?) Initial values
Static Members
zeros(size)
ones(size)
random(size, min, max)
randn(size, myu, sigma)
fromArray(arr)
Instance Members
dimension
sizes
length
value
iterator()
toArray()
toString()
toMatrix()
copy()
equals(other)
at(i)
set(i, value)
select(idx, axis)
slice(from, to, axis)
fill(value)
map(cb)
forEach(cb)
transpose(axises)
flip(axis)
shuffle(axis)
resize(sizes, init)
reshape(sizes)
concat(t, axis)
reduce(cb, init?, axis, keepdims)
broadcastOperate(o, fn)
operateAt(i, fn?)
dot(o)